Overview

Dataset statistics

Number of variables29
Number of observations8740
Missing cells8785
Missing cells (%)3.5%
Duplicate rows2
Duplicate rows (%)< 0.1%
Total size in memory10.4 MiB
Average record size in memory1.2 KiB

Variable types

Categorical13
Boolean2
Numeric12
URL2

Alerts

type has constant value "track"Constant
error has constant value "{'status': 404, 'message': 'analysis not found'}"Constant
Dataset has 2 (< 0.1%) duplicate rowsDuplicates
artist_name has a high cardinality: 3452 distinct valuesHigh cardinality
track_name has a high cardinality: 8420 distinct valuesHigh cardinality
hash has a high cardinality: 8737 distinct valuesHigh cardinality
id has a high cardinality: 8624 distinct valuesHigh cardinality
uri has a high cardinality: 8624 distinct valuesHigh cardinality
id_hash has a high cardinality: 8737 distinct valuesHigh cardinality
album has a high cardinality: 5378 distinct valuesHigh cardinality
release_date has a high cardinality: 2194 distinct valuesHigh cardinality
isrc has a high cardinality: 8626 distinct valuesHigh cardinality
energy is highly overall correlated with danceability and 3 other fieldsHigh correlation
loudness is highly overall correlated with danceability and 2 other fieldsHigh correlation
acousticness is highly overall correlated with energy and 1 other fieldsHigh correlation
tempo is highly overall correlated with time_signatureHigh correlation
time_signature is highly overall correlated with tempoHigh correlation
danceability is highly overall correlated with energy and 2 other fieldsHigh correlation
valence is highly overall correlated with danceability and 1 other fieldsHigh correlation
error has 8737 (> 99.9%) missing valuesMissing
track_name is uniformly distributedUniform
hash is uniformly distributedUniform
id is uniformly distributedUniform
uri is uniformly distributedUniform
id_hash is uniformly distributedUniform
isrc is uniformly distributedUniform
key has 1033 (11.8%) zerosZeros
instrumentalness has 1200 (13.7%) zerosZeros
popularity has 207 (2.4%) zerosZeros

Reproduction

Analysis started2022-12-17 20:10:10.161696
Analysis finished2022-12-17 20:11:38.613894
Duration1 minute and 28.45 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

artist_name
Categorical

Distinct3452
Distinct (%)39.5%
Missing0
Missing (%)0.0%
Memory size653.7 KiB
Justice
 
99
Radiohead
 
74
Tame Impala
 
73
IDLES
 
64
The Beatles
 
55
Other values (3447)
8375 

Length

Max length51
Median length33
Mean length10.722769
Min length2

Characters and Unicode

Total characters93717
Distinct characters351
Distinct categories17 ?
Distinct scripts9 ?
Distinct blocks9 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2399 ?
Unique (%)27.4%

Sample

1st row!!!
2nd row00110100 01010100
3rd row03 Greedo
4th row070 Shake
5th row070 Shake

Common Values

ValueCountFrequency (%)
Justice 99
 
1.1%
Radiohead 74
 
0.8%
Tame Impala 73
 
0.8%
IDLES 64
 
0.7%
The Beatles 55
 
0.6%
The Strokes 54
 
0.6%
King Krule 48
 
0.5%
Tycho 47
 
0.5%
Allah-Las 47
 
0.5%
Helado Negro 45
 
0.5%
Other values (3442) 8134
93.1%

Length

2022-12-17T21:11:38.852755image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 817
 
5.1%
100
 
0.6%
justice 99
 
0.6%
black 90
 
0.6%
boys 83
 
0.5%
impala 74
 
0.5%
radiohead 74
 
0.5%
tame 73
 
0.5%
of 72
 
0.4%
king 66
 
0.4%
Other values (4725) 14508
90.4%

Most occurring characters

ValueCountFrequency (%)
e 8611
 
9.2%
7316
 
7.8%
a 7228
 
7.7%
o 5310
 
5.7%
n 5116
 
5.5%
r 4925
 
5.3%
i 4894
 
5.2%
s 4322
 
4.6%
l 4107
 
4.4%
t 3362
 
3.6%
Other values (341) 38526
41.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 66726
71.2%
Uppercase Letter 18556
 
19.8%
Space Separator 7316
 
7.8%
Other Punctuation 397
 
0.4%
Other Letter 296
 
0.3%
Decimal Number 235
 
0.3%
Dash Punctuation 154
 
0.2%
Math Symbol 16
 
< 0.1%
Currency Symbol 6
 
< 0.1%
Modifier Letter 3
 
< 0.1%
Other values (7) 12
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
ا 24
 
8.1%
ل 14
 
4.7%
ي 12
 
4.1%
م 8
 
2.7%
س 6
 
2.0%
و 6
 
2.0%
ق 6
 
2.0%
5
 
1.7%
ن 5
 
1.7%
ى 5
 
1.7%
Other values (159) 205
69.3%
Lowercase Letter
ValueCountFrequency (%)
e 8611
12.9%
a 7228
10.8%
o 5310
 
8.0%
n 5116
 
7.7%
r 4925
 
7.4%
i 4894
 
7.3%
s 4322
 
6.5%
l 4107
 
6.2%
t 3362
 
5.0%
h 2676
 
4.0%
Other values (85) 16175
24.2%
Uppercase Letter
ValueCountFrequency (%)
T 1598
 
8.6%
S 1400
 
7.5%
B 1367
 
7.4%
M 1261
 
6.8%
A 1065
 
5.7%
D 1036
 
5.6%
C 975
 
5.3%
P 825
 
4.4%
J 814
 
4.4%
K 804
 
4.3%
Other values (41) 7411
39.9%
Other Punctuation
ValueCountFrequency (%)
. 198
49.9%
& 89
22.4%
' 51
 
12.8%
, 22
 
5.5%
! 13
 
3.3%
/ 11
 
2.8%
" 6
 
1.5%
? 3
 
0.8%
: 2
 
0.5%
1
 
0.3%
Decimal Number
ValueCountFrequency (%)
0 60
25.5%
1 35
14.9%
3 33
14.0%
9 29
12.3%
7 24
 
10.2%
8 18
 
7.7%
5 14
 
6.0%
6 10
 
4.3%
2 8
 
3.4%
4 4
 
1.7%
Math Symbol
ValueCountFrequency (%)
+ 13
81.2%
> 3
 
18.8%
Modifier Letter
ValueCountFrequency (%)
ـ 2
66.7%
1
33.3%
Spacing Mark
ValueCountFrequency (%)
ि 1
50.0%
1
50.0%
Space Separator
ValueCountFrequency (%)
7316
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 154
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%
Nonspacing Mark
ValueCountFrequency (%)
2
100.0%
Final Punctuation
ValueCountFrequency (%)
1
100.0%
Other Symbol
ValueCountFrequency (%)
° 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 85134
90.8%
Common 8135
 
8.7%
Cyrillic 130
 
0.1%
Arabic 129
 
0.1%
Han 106
 
0.1%
Hangul 38
 
< 0.1%
Greek 18
 
< 0.1%
Katakana 16
 
< 0.1%
Devanagari 11
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 8611
 
10.1%
a 7228
 
8.5%
o 5310
 
6.2%
n 5116
 
6.0%
r 4925
 
5.8%
i 4894
 
5.7%
s 4322
 
5.1%
l 4107
 
4.8%
t 3362
 
3.9%
h 2676
 
3.1%
Other values (85) 34583
40.6%
Han
ValueCountFrequency (%)
5
 
4.7%
3
 
2.8%
3
 
2.8%
2
 
1.9%
2
 
1.9%
2
 
1.9%
2
 
1.9%
2
 
1.9%
2
 
1.9%
2
 
1.9%
Other values (80) 81
76.4%
Cyrillic
ValueCountFrequency (%)
н 14
 
10.8%
и 12
 
9.2%
а 11
 
8.5%
о 9
 
6.9%
к 8
 
6.2%
р 7
 
5.4%
в 6
 
4.6%
т 6
 
4.6%
е 6
 
4.6%
ы 4
 
3.1%
Other values (27) 47
36.2%
Common
ValueCountFrequency (%)
7316
89.9%
. 198
 
2.4%
- 154
 
1.9%
& 89
 
1.1%
0 60
 
0.7%
' 51
 
0.6%
1 35
 
0.4%
3 33
 
0.4%
9 29
 
0.4%
7 24
 
0.3%
Other values (23) 146
 
1.8%
Hangul
ValueCountFrequency (%)
3
 
7.9%
3
 
7.9%
3
 
7.9%
2
 
5.3%
1
 
2.6%
1
 
2.6%
1
 
2.6%
1
 
2.6%
1
 
2.6%
1
 
2.6%
Other values (21) 21
55.3%
Arabic
ValueCountFrequency (%)
ا 24
18.6%
ل 14
 
10.9%
ي 12
 
9.3%
م 8
 
6.2%
س 6
 
4.7%
و 6
 
4.7%
ق 6
 
4.7%
ن 5
 
3.9%
ى 5
 
3.9%
ر 5
 
3.9%
Other values (17) 38
29.5%
Greek
ValueCountFrequency (%)
α 3
16.7%
η 2
11.1%
ς 2
11.1%
ζ 1
 
5.6%
Κ 1
 
5.6%
ρ 1
 
5.6%
μ 1
 
5.6%
π 1
 
5.6%
ο 1
 
5.6%
ύ 1
 
5.6%
Other values (4) 4
22.2%
Katakana
ValueCountFrequency (%)
2
12.5%
2
12.5%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
Other values (4) 4
25.0%
Devanagari
ValueCountFrequency (%)
2
18.2%
1
9.1%
1
9.1%
ि 1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 93013
99.2%
None 268
 
0.3%
Arabic 131
 
0.1%
Cyrillic 130
 
0.1%
CJK 106
 
0.1%
Hangul 38
 
< 0.1%
Katakana 18
 
< 0.1%
Devanagari 11
 
< 0.1%
Punctuation 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 8611
 
9.3%
7316
 
7.9%
a 7228
 
7.8%
o 5310
 
5.7%
n 5116
 
5.5%
r 4925
 
5.3%
i 4894
 
5.3%
s 4322
 
4.6%
l 4107
 
4.4%
t 3362
 
3.6%
Other values (69) 37822
40.7%
None
ValueCountFrequency (%)
ü 43
16.0%
é 33
 
12.3%
ö 26
 
9.7%
Ä 17
 
6.3%
ä 13
 
4.9%
ø 11
 
4.1%
á 10
 
3.7%
í 10
 
3.7%
ò 9
 
3.4%
ı 7
 
2.6%
Other values (48) 89
33.2%
Arabic
ValueCountFrequency (%)
ا 24
18.3%
ل 14
 
10.7%
ي 12
 
9.2%
م 8
 
6.1%
س 6
 
4.6%
و 6
 
4.6%
ق 6
 
4.6%
ن 5
 
3.8%
ى 5
 
3.8%
ر 5
 
3.8%
Other values (18) 40
30.5%
Cyrillic
ValueCountFrequency (%)
н 14
 
10.8%
и 12
 
9.2%
а 11
 
8.5%
о 9
 
6.9%
к 8
 
6.2%
р 7
 
5.4%
в 6
 
4.6%
т 6
 
4.6%
е 6
 
4.6%
ы 4
 
3.1%
Other values (27) 47
36.2%
CJK
ValueCountFrequency (%)
5
 
4.7%
3
 
2.8%
3
 
2.8%
2
 
1.9%
2
 
1.9%
2
 
1.9%
2
 
1.9%
2
 
1.9%
2
 
1.9%
2
 
1.9%
Other values (80) 81
76.4%
Hangul
ValueCountFrequency (%)
3
 
7.9%
3
 
7.9%
3
 
7.9%
2
 
5.3%
1
 
2.6%
1
 
2.6%
1
 
2.6%
1
 
2.6%
1
 
2.6%
1
 
2.6%
Other values (21) 21
55.3%
Devanagari
ValueCountFrequency (%)
2
18.2%
1
9.1%
1
9.1%
ि 1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
Katakana
ValueCountFrequency (%)
2
 
11.1%
2
 
11.1%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
Other values (6) 6
33.3%
Punctuation
ValueCountFrequency (%)
1
50.0%
1
50.0%

track_name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct8420
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Memory size705.0 KiB
Intro
 
9
You and I
 
7
Bones
 
5
Home
 
5
Gloria
 
5
Other values (8415)
8709 

Length

Max length131
Median length84
Mean length15.871053
Min length1

Characters and Unicode

Total characters138713
Distinct characters622
Distinct categories20 ?
Distinct scripts13 ?
Distinct blocks16 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8169 ?
Unique (%)93.5%

Sample

1st rowSo We Can Fuck
2nd row0000 871 0003
3rd rowSubstance (We Woke Up)
4th rowGuilty Conscience - Tame Impala Remix
5th rowGuilty Conscience - Tame Impala Remix Extended

Common Values

ValueCountFrequency (%)
Intro 9
 
0.1%
You and I 7
 
0.1%
Bones 5
 
0.1%
Home 5
 
0.1%
Gloria 5
 
0.1%
Echoes 5
 
0.1%
Outside 4
 
< 0.1%
Dreams 4
 
< 0.1%
Change 4
 
< 0.1%
You 4
 
< 0.1%
Other values (8410) 8688
99.4%

Length

2022-12-17T21:11:39.416789image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1070
 
4.1%
the 769
 
3.0%
you 320
 
1.2%
remix 299
 
1.2%
i 260
 
1.0%
feat 260
 
1.0%
in 251
 
1.0%
a 245
 
0.9%
to 234
 
0.9%
of 221
 
0.9%
Other values (7970) 21867
84.8%

Most occurring characters

ValueCountFrequency (%)
17056
 
12.3%
e 13274
 
9.6%
a 8073
 
5.8%
o 7749
 
5.6%
i 7638
 
5.5%
n 7183
 
5.2%
t 6271
 
4.5%
r 6150
 
4.4%
s 4983
 
3.6%
l 4519
 
3.3%
Other values (612) 55817
40.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 92263
66.5%
Uppercase Letter 23258
 
16.8%
Space Separator 17056
 
12.3%
Other Punctuation 1586
 
1.1%
Decimal Number 1409
 
1.0%
Dash Punctuation 988
 
0.7%
Close Punctuation 697
 
0.5%
Open Punctuation 697
 
0.5%
Other Letter 621
 
0.4%
Final Punctuation 59
 
< 0.1%
Other values (10) 79
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
ا 34
 
5.5%
ل 23
 
3.7%
ي 14
 
2.3%
م 13
 
2.1%
ر 12
 
1.9%
ה 9
 
1.4%
8
 
1.3%
ו 8
 
1.3%
ن 7
 
1.1%
6
 
1.0%
Other values (356) 487
78.4%
Lowercase Letter
ValueCountFrequency (%)
e 13274
14.4%
a 8073
 
8.7%
o 7749
 
8.4%
i 7638
 
8.3%
n 7183
 
7.8%
t 6271
 
6.8%
r 6150
 
6.7%
s 4983
 
5.4%
l 4519
 
4.9%
h 3371
 
3.7%
Other values (114) 23052
25.0%
Uppercase Letter
ValueCountFrequency (%)
S 2059
 
8.9%
T 1867
 
8.0%
M 1585
 
6.8%
L 1340
 
5.8%
A 1315
 
5.7%
B 1306
 
5.6%
R 1269
 
5.5%
D 1268
 
5.5%
C 1119
 
4.8%
I 1115
 
4.8%
Other values (49) 9015
38.8%
Other Punctuation
ValueCountFrequency (%)
. 510
32.2%
' 385
24.3%
, 301
19.0%
& 121
 
7.6%
/ 94
 
5.9%
! 69
 
4.4%
? 52
 
3.3%
: 23
 
1.5%
" 18
 
1.1%
% 4
 
0.3%
Other values (5) 9
 
0.6%
Nonspacing Mark
ValueCountFrequency (%)
3
13.0%
̈ 3
13.0%
3
13.0%
2
8.7%
2
8.7%
́ 2
8.7%
̊ 2
8.7%
̄ 1
 
4.3%
̌ 1
 
4.3%
̨ 1
 
4.3%
Other values (3) 3
13.0%
Decimal Number
ValueCountFrequency (%)
0 329
23.3%
2 292
20.7%
1 280
19.9%
9 118
 
8.4%
5 73
 
5.2%
3 71
 
5.0%
6 68
 
4.8%
7 66
 
4.7%
4 63
 
4.5%
8 49
 
3.5%
Math Symbol
ValueCountFrequency (%)
+ 4
44.4%
= 2
22.2%
2
22.2%
| 1
 
11.1%
Close Punctuation
ValueCountFrequency (%)
) 681
97.7%
] 14
 
2.0%
2
 
0.3%
Open Punctuation
ValueCountFrequency (%)
( 681
97.7%
[ 14
 
2.0%
2
 
0.3%
Final Punctuation
ValueCountFrequency (%)
54
91.5%
4
 
6.8%
» 1
 
1.7%
Currency Symbol
ValueCountFrequency (%)
$ 15
75.0%
4
 
20.0%
¢ 1
 
5.0%
Initial Punctuation
ValueCountFrequency (%)
4
66.7%
« 1
 
16.7%
1
 
16.7%
Spacing Mark
ValueCountFrequency (%)
2
50.0%
1
25.0%
ि 1
25.0%
Modifier Symbol
ValueCountFrequency (%)
^ 2
50.0%
` 1
25.0%
´ 1
25.0%
Other Symbol
ValueCountFrequency (%)
1
33.3%
1
33.3%
° 1
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 985
99.7%
3
 
0.3%
Modifier Letter
ValueCountFrequency (%)
1
50.0%
1
50.0%
Space Separator
ValueCountFrequency (%)
17056
100.0%
Format
ValueCountFrequency (%)
4
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 115203
83.1%
Common 22543
 
16.3%
Cyrillic 292
 
0.2%
Han 230
 
0.2%
Arabic 174
 
0.1%
Hangul 109
 
0.1%
Hebrew 62
 
< 0.1%
Devanagari 38
 
< 0.1%
Greek 28
 
< 0.1%
Thai 16
 
< 0.1%
Other values (3) 18
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
8
 
3.5%
6
 
2.6%
3
 
1.3%
3
 
1.3%
3
 
1.3%
3
 
1.3%
3
 
1.3%
2
 
0.9%
2
 
0.9%
2
 
0.9%
Other values (181) 195
84.8%
Latin
ValueCountFrequency (%)
e 13274
 
11.5%
a 8073
 
7.0%
o 7749
 
6.7%
i 7638
 
6.6%
n 7183
 
6.2%
t 6271
 
5.4%
r 6150
 
5.3%
s 4983
 
4.3%
l 4519
 
3.9%
h 3371
 
2.9%
Other values (111) 45992
39.9%
Hangul
ValueCountFrequency (%)
4
 
3.7%
3
 
2.8%
3
 
2.8%
2
 
1.8%
2
 
1.8%
2
 
1.8%
2
 
1.8%
2
 
1.8%
2
 
1.8%
2
 
1.8%
Other values (81) 85
78.0%
Common
ValueCountFrequency (%)
17056
75.7%
- 985
 
4.4%
) 681
 
3.0%
( 681
 
3.0%
. 510
 
2.3%
' 385
 
1.7%
0 329
 
1.5%
, 301
 
1.3%
2 292
 
1.3%
1 280
 
1.2%
Other values (46) 1043
 
4.6%
Cyrillic
ValueCountFrequency (%)
а 31
 
10.6%
н 25
 
8.6%
е 24
 
8.2%
т 23
 
7.9%
о 22
 
7.5%
с 17
 
5.8%
р 15
 
5.1%
и 15
 
5.1%
л 14
 
4.8%
в 10
 
3.4%
Other values (35) 96
32.9%
Arabic
ValueCountFrequency (%)
ا 34
19.5%
ل 23
13.2%
ي 14
 
8.0%
م 13
 
7.5%
ر 12
 
6.9%
ن 7
 
4.0%
و 6
 
3.4%
ء 6
 
3.4%
ب 6
 
3.4%
ح 5
 
2.9%
Other values (21) 48
27.6%
Devanagari
ValueCountFrequency (%)
4
 
10.5%
3
 
7.9%
3
 
7.9%
3
 
7.9%
3
 
7.9%
2
 
5.3%
2
 
5.3%
2
 
5.3%
2
 
5.3%
1
 
2.6%
Other values (13) 13
34.2%
Hebrew
ValueCountFrequency (%)
ה 9
14.5%
ו 8
12.9%
ל 6
9.7%
י 6
9.7%
ש 5
 
8.1%
ח 4
 
6.5%
א 3
 
4.8%
ן 3
 
4.8%
ד 3
 
4.8%
ת 2
 
3.2%
Other values (9) 13
21.0%
Greek
ValueCountFrequency (%)
ο 3
 
10.7%
α 3
 
10.7%
ι 3
 
10.7%
υ 2
 
7.1%
ρ 2
 
7.1%
ε 2
 
7.1%
π 2
 
7.1%
ά 1
 
3.6%
γ 1
 
3.6%
λ 1
 
3.6%
Other values (8) 8
28.6%
Thai
ValueCountFrequency (%)
2
12.5%
2
12.5%
2
12.5%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
Other values (3) 3
18.8%
Inherited
ValueCountFrequency (%)
̈ 3
27.3%
́ 2
18.2%
̊ 2
18.2%
̄ 1
 
9.1%
̌ 1
 
9.1%
̨ 1
 
9.1%
̇ 1
 
9.1%
Katakana
ValueCountFrequency (%)
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
Hiragana
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 137263
99.0%
None 431
 
0.3%
Cyrillic 292
 
0.2%
CJK 229
 
0.2%
Arabic 174
 
0.1%
Hangul 109
 
0.1%
Punctuation 71
 
0.1%
Hebrew 62
 
< 0.1%
Devanagari 38
 
< 0.1%
Thai 16
 
< 0.1%
Other values (6) 28
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
17056
 
12.4%
e 13274
 
9.7%
a 8073
 
5.9%
o 7749
 
5.6%
i 7638
 
5.6%
n 7183
 
5.2%
t 6271
 
4.6%
r 6150
 
4.5%
s 4983
 
3.6%
l 4519
 
3.3%
Other values (77) 54367
39.6%
Punctuation
ValueCountFrequency (%)
54
76.1%
4
 
5.6%
4
 
5.6%
4
 
5.6%
3
 
4.2%
1
 
1.4%
1
 
1.4%
None
ValueCountFrequency (%)
ü 50
 
11.6%
ä 47
 
10.9%
é 47
 
10.9%
ö 21
 
4.9%
í 19
 
4.4%
ı 17
 
3.9%
å 16
 
3.7%
ç 12
 
2.8%
Ü 10
 
2.3%
ó 10
 
2.3%
Other values (86) 182
42.2%
Arabic
ValueCountFrequency (%)
ا 34
19.5%
ل 23
13.2%
ي 14
 
8.0%
م 13
 
7.5%
ر 12
 
6.9%
ن 7
 
4.0%
و 6
 
3.4%
ء 6
 
3.4%
ب 6
 
3.4%
ح 5
 
2.9%
Other values (21) 48
27.6%
Cyrillic
ValueCountFrequency (%)
а 31
 
10.6%
н 25
 
8.6%
е 24
 
8.2%
т 23
 
7.9%
о 22
 
7.5%
с 17
 
5.8%
р 15
 
5.1%
и 15
 
5.1%
л 14
 
4.8%
в 10
 
3.4%
Other values (35) 96
32.9%
Hebrew
ValueCountFrequency (%)
ה 9
14.5%
ו 8
12.9%
ל 6
9.7%
י 6
9.7%
ש 5
 
8.1%
ח 4
 
6.5%
א 3
 
4.8%
ן 3
 
4.8%
ד 3
 
4.8%
ת 2
 
3.2%
Other values (9) 13
21.0%
CJK
ValueCountFrequency (%)
8
 
3.5%
6
 
2.6%
3
 
1.3%
3
 
1.3%
3
 
1.3%
3
 
1.3%
3
 
1.3%
2
 
0.9%
2
 
0.9%
2
 
0.9%
Other values (180) 194
84.7%
Currency Symbols
ValueCountFrequency (%)
4
100.0%
Devanagari
ValueCountFrequency (%)
4
 
10.5%
3
 
7.9%
3
 
7.9%
3
 
7.9%
3
 
7.9%
2
 
5.3%
2
 
5.3%
2
 
5.3%
2
 
5.3%
1
 
2.6%
Other values (13) 13
34.2%
Hangul
ValueCountFrequency (%)
4
 
3.7%
3
 
2.8%
3
 
2.8%
2
 
1.8%
2
 
1.8%
2
 
1.8%
2
 
1.8%
2
 
1.8%
2
 
1.8%
2
 
1.8%
Other values (81) 85
78.0%
Diacriticals
ValueCountFrequency (%)
̈ 3
27.3%
́ 2
18.2%
̊ 2
18.2%
̄ 1
 
9.1%
̌ 1
 
9.1%
̨ 1
 
9.1%
̇ 1
 
9.1%
Thai
ValueCountFrequency (%)
2
12.5%
2
12.5%
2
12.5%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
Other values (3) 3
18.8%
Arrows
ValueCountFrequency (%)
2
100.0%
Katakana
ValueCountFrequency (%)
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
Geometric Shapes
ValueCountFrequency (%)
1
50.0%
1
50.0%
Hiragana
ValueCountFrequency (%)
1
100.0%

plays
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size76.8 KiB
True
8440 
False
 
300
ValueCountFrequency (%)
True 8440
96.6%
False 300
 
3.4%
2022-12-17T21:11:39.977012image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

hash
Categorical

HIGH CARDINALITY
UNIFORM

Distinct8737
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
23134ee7b28d5abec1f2cf07b690bc8a74f8c1a5b18ef42abaf1b256990c8293
 
4
40826686141ecbeb2a9a032ba9b17ae9966d3b9285f651017bb33d4ed988f717
 
1
12b4a008b377429b7bb86e1fd98d740b7f40f2c3aea7e5f261315180c9205aa5
 
1
c98b40e2b7cc30c1a38fe426e461a343c47bc18650d30734932ada93bb9b4219
 
1
d33f1a814879dc4b2cb79d8a11cdae67bcefa65b3102e4be1db7adee9cd0e667
 
1
Other values (8732)
8732 

Length

Max length64
Median length64
Mean length64
Min length64

Characters and Unicode

Total characters559360
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8736 ?
Unique (%)> 99.9%

Sample

1st row40826686141ecbeb2a9a032ba9b17ae9966d3b9285f651017bb33d4ed988f717
2nd row8fe126c7d28ac465eacb644ec878ea554744d6dd7d2fef696df6e847bd133694
3rd row5139a49bdfa4749b67c074870911e75976d58b32b076d1d7a72f4813edfe76a3
4th row2b34ac0f1ca8fac70845b6cb894bac839ab229454203ef29b3d2bee9058fd560
5th row283330c524139861dda440b4ea23424f6930f9b900372737d91e20213fe1c6a4

Common Values

ValueCountFrequency (%)
23134ee7b28d5abec1f2cf07b690bc8a74f8c1a5b18ef42abaf1b256990c8293 4
 
< 0.1%
40826686141ecbeb2a9a032ba9b17ae9966d3b9285f651017bb33d4ed988f717 1
 
< 0.1%
12b4a008b377429b7bb86e1fd98d740b7f40f2c3aea7e5f261315180c9205aa5 1
 
< 0.1%
c98b40e2b7cc30c1a38fe426e461a343c47bc18650d30734932ada93bb9b4219 1
 
< 0.1%
d33f1a814879dc4b2cb79d8a11cdae67bcefa65b3102e4be1db7adee9cd0e667 1
 
< 0.1%
93bec58465fb2885a807f83ded2789c965ffeff20d0b493d64b796bffb7ea65c 1
 
< 0.1%
69ec705935becbd3f8f351005fba740c37fac76f2143bacbe965d25770512717 1
 
< 0.1%
bafd917c861839e4d430a9453dc52d053a482e1e2b773cefd4fbb0c2cc3dcf4b 1
 
< 0.1%
f8e84ed5ff8cbaaaaa63b2145b553547ee92ca0424250b81fe82f4940a9786b5 1
 
< 0.1%
05467790cdb77fda2459018283ab271ae1fe16ab87b7038c77928caef806a553 1
 
< 0.1%
Other values (8727) 8727
99.9%

Length

2022-12-17T21:11:40.355736image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
23134ee7b28d5abec1f2cf07b690bc8a74f8c1a5b18ef42abaf1b256990c8293 4
 
< 0.1%
118e08bd62d0adc42bb4ca154793d8f464f408b3c20e581606ea2c8862d8eff8 1
 
< 0.1%
72978a16871bc08ad3d64b24fd6eca05c8d50a08bd8cd7978ac238e1289134eb 1
 
< 0.1%
352e3c20633bbb0fb9932f683092f9470cb3bb0b4a9962ef6b022702e609efaa 1
 
< 0.1%
4fc625da9ae169ec64d1c0f922d6d1ac8fa660b06408b0e868960cf2ef16a279 1
 
< 0.1%
94f029a0618f5ecf11ee990cb8aa96e33f661dc338dc3133e8923007bd9bd104 1
 
< 0.1%
c1e86348af4afbe52e38485e25197ba3b262717e038656f413771f97f0a68e19 1
 
< 0.1%
4c77ed4fabdd9a9967078bd46a7abc6b6cf72a632245c58ddab554961b5f2d98 1
 
< 0.1%
cbe55ee5f173ea3090ec1a5bac0b7829b32295e7f869103c13068034843e2c85 1
 
< 0.1%
a35fcb9b76c46fb421ceb6a1a48e20af460b3bb6cf43627b262333e354409984 1
 
< 0.1%
Other values (8727) 8727
99.9%

Most occurring characters

ValueCountFrequency (%)
7 35274
 
6.3%
b 35149
 
6.3%
2 35093
 
6.3%
f 35051
 
6.3%
5 35030
 
6.3%
c 35019
 
6.3%
3 35008
 
6.3%
1 34984
 
6.3%
8 34982
 
6.3%
4 34921
 
6.2%
Other values (6) 208849
37.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 349574
62.5%
Lowercase Letter 209786
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 35274
10.1%
2 35093
10.0%
5 35030
10.0%
3 35008
10.0%
1 34984
10.0%
8 34982
10.0%
4 34921
10.0%
9 34800
10.0%
0 34750
9.9%
6 34732
9.9%
Lowercase Letter
ValueCountFrequency (%)
b 35149
16.8%
f 35051
16.7%
c 35019
16.7%
e 34908
16.6%
a 34890
16.6%
d 34769
16.6%

Most occurring scripts

ValueCountFrequency (%)
Common 349574
62.5%
Latin 209786
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
7 35274
10.1%
2 35093
10.0%
5 35030
10.0%
3 35008
10.0%
1 34984
10.0%
8 34982
10.0%
4 34921
10.0%
9 34800
10.0%
0 34750
9.9%
6 34732
9.9%
Latin
ValueCountFrequency (%)
b 35149
16.8%
f 35051
16.7%
c 35019
16.7%
e 34908
16.6%
a 34890
16.6%
d 34769
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 559360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 35274
 
6.3%
b 35149
 
6.3%
2 35093
 
6.3%
f 35051
 
6.3%
5 35030
 
6.3%
c 35019
 
6.3%
3 35008
 
6.3%
1 34984
 
6.3%
8 34982
 
6.3%
4 34921
 
6.2%
Other values (6) 208849
37.3%

danceability
Real number (ℝ)

Distinct898
Distinct (%)10.3%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.56833128
Minimum0
Maximum0.984
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size136.6 KiB
2022-12-17T21:11:40.762608image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.229
Q10.458
median0.587
Q30.697
95-th percentile0.826
Maximum0.984
Range0.984
Interquartile range (IQR)0.239

Descriptive statistics

Standard deviation0.17668212
Coefficient of variation (CV)0.31087875
Kurtosis-0.11141122
Mean0.56833128
Median Absolute Deviation (MAD)0.118
Skewness-0.46683558
Sum4965.5104
Variance0.03121657
MonotonicityNot monotonic
2022-12-17T21:11:41.218446image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.633 37
 
0.4%
0.625 30
 
0.3%
0.545 29
 
0.3%
0.641 29
 
0.3%
0.705 29
 
0.3%
0.623 28
 
0.3%
0.647 28
 
0.3%
0.646 28
 
0.3%
0.534 28
 
0.3%
0.675 27
 
0.3%
Other values (888) 8444
96.6%
ValueCountFrequency (%)
0 1
< 0.1%
0.0565 2
< 0.1%
0.0611 1
< 0.1%
0.0612 1
< 0.1%
0.0616 1
< 0.1%
0.0627 1
< 0.1%
0.0634 1
< 0.1%
0.0639 1
< 0.1%
0.0645 1
< 0.1%
0.0646 1
< 0.1%
ValueCountFrequency (%)
0.984 2
< 0.1%
0.983 1
 
< 0.1%
0.982 1
 
< 0.1%
0.979 1
 
< 0.1%
0.978 1
 
< 0.1%
0.975 1
 
< 0.1%
0.972 1
 
< 0.1%
0.969 1
 
< 0.1%
0.967 2
< 0.1%
0.966 3
< 0.1%

energy
Real number (ℝ)

Distinct1153
Distinct (%)13.2%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.62674201
Minimum0.000431
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size136.6 KiB
2022-12-17T21:11:41.644674image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.000431
5-th percentile0.1768
Q10.486
median0.66
Q30.808
95-th percentile0.939
Maximum1
Range0.999569
Interquartile range (IQR)0.322

Descriptive statistics

Standard deviation0.23014255
Coefficient of variation (CV)0.3672046
Kurtosis-0.0380633
Mean0.62674201
Median Absolute Deviation (MAD)0.159
Skewness-0.68742071
Sum5475.845
Variance0.052965595
MonotonicityNot monotonic
2022-12-17T21:11:42.084376image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.859 26
 
0.3%
0.781 24
 
0.3%
0.792 24
 
0.3%
0.759 23
 
0.3%
0.724 23
 
0.3%
0.576 23
 
0.3%
0.596 22
 
0.3%
0.659 22
 
0.3%
0.84 22
 
0.3%
0.714 21
 
0.2%
Other values (1143) 8507
97.3%
ValueCountFrequency (%)
0.000431 1
< 0.1%
0.000603 1
< 0.1%
0.00105 1
< 0.1%
0.00134 1
< 0.1%
0.00142 1
< 0.1%
0.00151 1
< 0.1%
0.00155 1
< 0.1%
0.00166 1
< 0.1%
0.00172 1
< 0.1%
0.0019 1
< 0.1%
ValueCountFrequency (%)
1 2
 
< 0.1%
0.999 1
 
< 0.1%
0.998 5
0.1%
0.997 2
 
< 0.1%
0.996 3
< 0.1%
0.995 5
0.1%
0.994 6
0.1%
0.993 5
0.1%
0.992 2
 
< 0.1%
0.991 4
< 0.1%

key
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5.2871695
Minimum0
Maximum11
Zeros1033
Zeros (%)11.8%
Negative0
Negative (%)0.0%
Memory size136.6 KiB
2022-12-17T21:11:42.443963image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q39
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.5887889
Coefficient of variation (CV)0.6787732
Kurtosis-1.2951623
Mean5.2871695
Median Absolute Deviation (MAD)3
Skewness0.0082237099
Sum46194
Variance12.879406
MonotonicityNot monotonic
2022-12-17T21:11:42.976291image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 1033
11.8%
7 999
11.4%
2 946
10.8%
9 940
10.8%
1 807
9.2%
11 750
8.6%
4 698
8.0%
5 688
7.9%
6 580
6.6%
10 521
6.0%
Other values (2) 775
8.9%
ValueCountFrequency (%)
0 1033
11.8%
1 807
9.2%
2 946
10.8%
3 266
 
3.0%
4 698
8.0%
5 688
7.9%
6 580
6.6%
7 999
11.4%
8 509
5.8%
9 940
10.8%
ValueCountFrequency (%)
11 750
8.6%
10 521
6.0%
9 940
10.8%
8 509
5.8%
7 999
11.4%
6 580
6.6%
5 688
7.9%
4 698
8.0%
3 266
 
3.0%
2 946
10.8%

loudness
Real number (ℝ)

Distinct6123
Distinct (%)70.1%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-8.9690396
Minimum-46.847
Maximum2.358
Zeros0
Zeros (%)0.0%
Negative8734
Negative (%)99.9%
Memory size136.6 KiB
2022-12-17T21:11:43.331733image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-46.847
5-th percentile-18.347
Q1-10.203
median-7.702
Q3-5.905
95-th percentile-3.8216
Maximum2.358
Range49.205
Interquartile range (IQR)4.298

Descriptive statistics

Standard deviation5.397237
Coefficient of variation (CV)-0.6017631
Kurtosis9.5760277
Mean-8.9690396
Median Absolute Deviation (MAD)2.066
Skewness-2.7107999
Sum-78362.499
Variance29.130168
MonotonicityNot monotonic
2022-12-17T21:11:43.775723image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-9.043 7
 
0.1%
-7.081 6
 
0.1%
-5.897 5
 
0.1%
-6.34 5
 
0.1%
-9.095 5
 
0.1%
-6.466 5
 
0.1%
-6.076 5
 
0.1%
-8.055 5
 
0.1%
-7.441 5
 
0.1%
-6.474 5
 
0.1%
Other values (6113) 8684
99.4%
ValueCountFrequency (%)
-46.847 1
< 0.1%
-42.291 1
< 0.1%
-42.013 1
< 0.1%
-41.748 1
< 0.1%
-39.904 1
< 0.1%
-39.69 1
< 0.1%
-39.154 1
< 0.1%
-38.656 1
< 0.1%
-38.605 1
< 0.1%
-38.523 1
< 0.1%
ValueCountFrequency (%)
2.358 1
< 0.1%
2.357 1
< 0.1%
1.342 1
< 0.1%
-0.578 1
< 0.1%
-0.657 1
< 0.1%
-0.737 1
< 0.1%
-0.738 1
< 0.1%
-0.74 1
< 0.1%
-0.91 1
< 0.1%
-1.269 1
< 0.1%

mode
Categorical

Distinct2
Distinct (%)< 0.1%
Missing3
Missing (%)< 0.1%
Memory size580.3 KiB
1.0
5341 
0.0
3396 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters26211
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 5341
61.1%
0.0 3396
38.9%
(Missing) 3
 
< 0.1%

Length

2022-12-17T21:11:44.185979image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-17T21:11:44.528090image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 5341
61.1%
0.0 3396
38.9%

Most occurring characters

ValueCountFrequency (%)
0 12133
46.3%
. 8737
33.3%
1 5341
20.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17474
66.7%
Other Punctuation 8737
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12133
69.4%
1 5341
30.6%
Other Punctuation
ValueCountFrequency (%)
. 8737
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 26211
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12133
46.3%
. 8737
33.3%
1 5341
20.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26211
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12133
46.3%
. 8737
33.3%
1 5341
20.4%

speechiness
Real number (ℝ)

Distinct1148
Distinct (%)13.1%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.078851684
Minimum0
Maximum0.67650954
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size136.6 KiB
2022-12-17T21:11:44.876832image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.027323296
Q10.033724869
median0.043059489
Q30.071483102
95-th percentile0.27383667
Maximum0.67650954
Range0.67650954
Interquartile range (IQR)0.037758232

Descriptive statistics

Standard deviation0.1014294
Coefficient of variation (CV)1.2863315
Kurtosis17.278956
Mean0.078851684
Median Absolute Deviation (MAD)0.012239347
Skewness3.8689485
Sum688.92717
Variance0.010287924
MonotonicityNot monotonic
2022-12-17T21:11:45.294834image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.03178932249 40
 
0.5%
0.0339182182 40
 
0.5%
0.02907324749 37
 
0.4%
0.03159556159 35
 
0.4%
0.03130484983 35
 
0.4%
0.02926749768 35
 
0.4%
0.03014115691 35
 
0.4%
0.03584965289 34
 
0.4%
0.02936460863 34
 
0.4%
0.0283930745 33
 
0.4%
Other values (1138) 8379
95.9%
ValueCountFrequency (%)
0 1
< 0.1%
0.02185933435 1
< 0.1%
0.02225060893 1
< 0.1%
0.02234840366 1
< 0.1%
0.02254396443 1
< 0.1%
0.02273948697 2
< 0.1%
0.0228372339 1
< 0.1%
0.0233258253 1
< 0.1%
0.02342351494 1
< 0.1%
0.02352119504 1
< 0.1%
ValueCountFrequency (%)
0.6765095394 3
 
< 0.1%
0.6760010217 6
0.1%
0.6754922453 3
 
< 0.1%
0.6749832099 6
0.1%
0.6744739153 6
0.1%
0.6739643611 8
0.1%
0.6734545472 6
0.1%
0.6729444732 6
0.1%
0.672434139 6
0.1%
0.6719235441 4
< 0.1%

acousticness
Real number (ℝ)

Distinct2853
Distinct (%)32.7%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.2104478
Minimum1.1999993 × 10-6
Maximum0.69114518
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size136.6 KiB
2022-12-17T21:11:45.752165image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1.1999993 × 10-6
5-th percentile0.00034993876
Q10.016266972
median0.11689375
Q30.37569295
95-th percentile0.65347034
Maximum0.69114518
Range0.69114398
Interquartile range (IQR)0.35942598

Descriptive statistics

Standard deviation0.22247432
Coefficient of variation (CV)1.0571473
Kurtosis-0.735469
Mean0.2104478
Median Absolute Deviation (MAD)0.11406775
Skewness0.81557972
Sum1838.6824
Variance0.049494824
MonotonicityNot monotonic
2022-12-17T21:11:46.230441image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1414995623 17
 
0.2%
0.1552928844 17
 
0.2%
0.1213322852 17
 
0.2%
0.1034587084 16
 
0.2%
0.1663615372 16
 
0.2%
0.1133286853 15
 
0.2%
0.1168937515 15
 
0.2%
0.01232374969 15
 
0.2%
0.1292723357 15
 
0.2%
0.6896410412 14
 
0.2%
Other values (2843) 8580
98.2%
ValueCountFrequency (%)
1.19999928 × 10-61
< 0.1%
1.279999181 × 10-61
< 0.1%
1.299999155 × 10-61
< 0.1%
1.559998783 × 10-61
< 0.1%
1.769998434 × 10-61
< 0.1%
2.379997168 × 10-61
< 0.1%
2.729996274 × 10-61
< 0.1%
2.829995996 × 10-61
< 0.1%
2.98999553 × 10-61
< 0.1%
3.209994848 × 10-61
< 0.1%
ValueCountFrequency (%)
0.6911451779 6
0.1%
0.6906440503 8
0.1%
0.6901426715 12
0.1%
0.6896410412 14
0.2%
0.6891391592 14
0.2%
0.6886370251 5
 
0.1%
0.6881346387 8
0.1%
0.6876319999 10
0.1%
0.6871291082 14
0.2%
0.6866259636 10
0.1%

instrumentalness
Real number (ℝ)

Distinct3203
Distinct (%)36.7%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.19660258
Minimum0
Maximum0.69264706
Zeros1200
Zeros (%)13.7%
Negative0
Negative (%)0.0%
Memory size136.6 KiB
2022-12-17T21:11:46.664671image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16.9197606 × 10-5
median0.021272135
Q30.45234869
95-th percentile0.64553133
Maximum0.69264706
Range0.69264706
Interquartile range (IQR)0.4522795

Descriptive statistics

Standard deviation0.25247085
Coefficient of variation (CV)1.2841686
Kurtosis-1.0644889
Mean0.19660258
Median Absolute Deviation (MAD)0.021272135
Skewness0.81798245
Sum1717.7167
Variance0.06374153
MonotonicityNot monotonic
2022-12-17T21:11:47.137308image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1200
 
13.7%
0.6450068052 18
 
0.2%
0.6502396795 17
 
0.2%
0.6243328646 16
 
0.2%
0.6323350412 15
 
0.2%
0.6264730473 15
 
0.2%
0.6544063522 15
 
0.2%
0.6392188385 15
 
0.2%
0.6481498146 14
 
0.2%
0.6232610531 14
 
0.2%
Other values (3193) 7398
84.6%
ValueCountFrequency (%)
0 1200
13.7%
9.999995 × 10-72
 
< 0.1%
1.00999949 × 10-61
 
< 0.1%
1.02999947 × 10-63
 
< 0.1%
1.039999459 × 10-61
 
< 0.1%
1.049999449 × 10-61
 
< 0.1%
1.059999438 × 10-61
 
< 0.1%
1.069999428 × 10-65
 
0.1%
1.079999417 × 10-61
 
< 0.1%
1.089999406 × 10-62
 
< 0.1%
ValueCountFrequency (%)
0.6926470555 1
 
< 0.1%
0.6906440503 3
< 0.1%
0.6901426715 1
 
< 0.1%
0.6896410412 1
 
< 0.1%
0.6886370251 2
< 0.1%
0.6876319999 1
 
< 0.1%
0.6861225656 1
 
< 0.1%
0.6846108495 1
 
< 0.1%
0.6841064359 2
< 0.1%
0.6836017677 1
 
< 0.1%

liveness
Real number (ℝ)

Distinct1256
Distinct (%)14.4%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.16670213
Minimum0.020586634
Maximum0.68712911
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size136.6 KiB
2022-12-17T21:11:47.609328image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.020586634
5-th percentile0.061941402
Q10.092943746
median0.11689375
Q30.20945022
95-th percentile0.42853038
Maximum0.68712911
Range0.66654247
Interquartile range (IQR)0.11650648

Descriptive statistics

Standard deviation0.11737167
Coefficient of variation (CV)0.70408018
Kurtosis3.3707645
Mean0.16670213
Median Absolute Deviation (MAD)0.035866913
Skewness1.8279782
Sum1456.4765
Variance0.013776108
MonotonicityNot monotonic
2022-12-17T21:11:48.109126image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1052605107 123
 
1.4%
0.1034587084 113
 
1.3%
0.1043600153 112
 
1.3%
0.09984533497 108
 
1.2%
0.09712671073 104
 
1.2%
0.1016536537 102
 
1.2%
0.1025565883 102
 
1.2%
0.1007499031 101
 
1.2%
0.09803374027 96
 
1.1%
0.09621885774 94
 
1.1%
Other values (1246) 7682
87.9%
ValueCountFrequency (%)
0.02058663361 1
< 0.1%
0.02127213528 1
< 0.1%
0.02146790662 1
< 0.1%
0.02166363964 1
< 0.1%
0.0242046887 1
< 0.1%
0.02439988682 1
< 0.1%
0.02596010167 1
< 0.1%
0.02644717001 1
< 0.1%
0.02673929719 1
< 0.1%
0.02761516703 1
< 0.1%
ValueCountFrequency (%)
0.6871291082 1
< 0.1%
0.6866259636 1
< 0.1%
0.6856189141 1
< 0.1%
0.6846108495 1
< 0.1%
0.6836017677 1
< 0.1%
0.6820862332 1
< 0.1%
0.6810745993 1
< 0.1%
0.679555227 1
< 0.1%
0.6790482562 1
< 0.1%
0.6785410282 2
< 0.1%

valence
Real number (ℝ)

Distinct1257
Distinct (%)14.4%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.47822773
Minimum0
Maximum0.982
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size136.6 KiB
2022-12-17T21:11:48.736958image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.07266
Q10.279
median0.477
Q30.677
95-th percentile0.903
Maximum0.982
Range0.982
Interquartile range (IQR)0.398

Descriptive statistics

Standard deviation0.2536176
Coefficient of variation (CV)0.53032808
Kurtosis-0.96629524
Mean0.47822773
Median Absolute Deviation (MAD)0.199
Skewness0.075683072
Sum4178.2757
Variance0.064321885
MonotonicityNot monotonic
2022-12-17T21:11:49.139417image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.424 22
 
0.3%
0.963 22
 
0.3%
0.961 21
 
0.2%
0.389 20
 
0.2%
0.611 19
 
0.2%
0.527 19
 
0.2%
0.67 18
 
0.2%
0.497 18
 
0.2%
0.414 18
 
0.2%
0.529 18
 
0.2%
Other values (1247) 8542
97.7%
ValueCountFrequency (%)
0 3
< 0.1%
0.006 1
 
< 0.1%
0.0147 1
 
< 0.1%
0.0166 1
 
< 0.1%
0.0274 1
 
< 0.1%
0.0283 1
 
< 0.1%
0.0291 1
 
< 0.1%
0.0298 1
 
< 0.1%
0.03 1
 
< 0.1%
0.0303 1
 
< 0.1%
ValueCountFrequency (%)
0.982 1
 
< 0.1%
0.981 1
 
< 0.1%
0.98 1
 
< 0.1%
0.979 2
 
< 0.1%
0.978 2
 
< 0.1%
0.977 2
 
< 0.1%
0.976 3
< 0.1%
0.975 3
< 0.1%
0.974 5
0.1%
0.973 3
< 0.1%

tempo
Real number (ℝ)

Distinct7523
Distinct (%)86.1%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean120.90561
Minimum0
Maximum219.921
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size136.6 KiB
2022-12-17T21:11:49.580817image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile77.1548
Q1100.01
median120.002
Q3138.192
95-th percentile172.6386
Maximum219.921
Range219.921
Interquartile range (IQR)38.182

Descriptive statistics

Standard deviation28.471434
Coefficient of variation (CV)0.2354848
Kurtosis-0.20975716
Mean120.90561
Median Absolute Deviation (MAD)19.839
Skewness0.32836783
Sum1056352.4
Variance810.62257
MonotonicityNot monotonic
2022-12-17T21:11:49.986292image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120.006 9
 
0.1%
120.002 7
 
0.1%
109.992 6
 
0.1%
127.991 6
 
0.1%
120.044 6
 
0.1%
120.007 6
 
0.1%
124.013 5
 
0.1%
119.981 5
 
0.1%
120 5
 
0.1%
119.976 5
 
0.1%
Other values (7513) 8677
99.3%
ValueCountFrequency (%)
0 1
< 0.1%
35.862 1
< 0.1%
42.749 1
< 0.1%
44.499 1
< 0.1%
45.01 1
< 0.1%
47.15 1
< 0.1%
49.251 1
< 0.1%
50.685 1
< 0.1%
50.786 1
< 0.1%
54.084 1
< 0.1%
ValueCountFrequency (%)
219.921 1
< 0.1%
216.087 1
< 0.1%
216.053 1
< 0.1%
210.164 1
< 0.1%
210.028 1
< 0.1%
206.867 1
< 0.1%
206.733 1
< 0.1%
206.488 1
< 0.1%
206.247 1
< 0.1%
206.195 1
< 0.1%

type
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size597.5 KiB
track
8740 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters43700
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtrack
2nd rowtrack
3rd rowtrack
4th rowtrack
5th rowtrack

Common Values

ValueCountFrequency (%)
track 8740
100.0%

Length

2022-12-17T21:11:50.382396image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-17T21:11:50.817708image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
track 8740
100.0%

Most occurring characters

ValueCountFrequency (%)
t 8740
20.0%
r 8740
20.0%
a 8740
20.0%
c 8740
20.0%
k 8740
20.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 43700
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 8740
20.0%
r 8740
20.0%
a 8740
20.0%
c 8740
20.0%
k 8740
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 43700
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 8740
20.0%
r 8740
20.0%
a 8740
20.0%
c 8740
20.0%
k 8740
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 8740
20.0%
r 8740
20.0%
a 8740
20.0%
c 8740
20.0%
k 8740
20.0%

id
Categorical

HIGH CARDINALITY
UNIFORM

Distinct8624
Distinct (%)98.7%
Missing3
Missing (%)< 0.1%
Memory size742.4 KiB
35tzxthMBglBMjmZ7Fn1hj
 
4
50fkJxrv0ZLTt9EHZGBOP7
 
3
2a1iMaoWQ5MnvLFBDv4qkf
 
3
7CBfP01G2qOB9GFWAN7ocL
 
2
5hChuUMe5iHfjkXgzj1D4b
 
2
Other values (8619)
8723 

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters192214
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8515 ?
Unique (%)97.5%

Sample

1st row4knd2gQyr2DTRLfJDHcyMS
2nd row7ns3vcnzAxjCZVYwlwazah
3rd row2S8gTIectkC846PHdsAshC
4th row5i5fCpsnqDJ9AfeObgd0gW
5th row7qDUOLnOLYKTwzvCJDnYRf

Common Values

ValueCountFrequency (%)
35tzxthMBglBMjmZ7Fn1hj 4
 
< 0.1%
50fkJxrv0ZLTt9EHZGBOP7 3
 
< 0.1%
2a1iMaoWQ5MnvLFBDv4qkf 3
 
< 0.1%
7CBfP01G2qOB9GFWAN7ocL 2
 
< 0.1%
5hChuUMe5iHfjkXgzj1D4b 2
 
< 0.1%
4JSo7H334HZiZtDIUcMpv8 2
 
< 0.1%
7tDEl7uRnoGdi0SFghDCz6 2
 
< 0.1%
4OUmlC67FoPLvQNuE5C7kF 2
 
< 0.1%
0GDz2tPdh6FbxJxM75SQB4 2
 
< 0.1%
4I5wK1FPimEXQb2zID7Duy 2
 
< 0.1%
Other values (8614) 8713
99.7%
(Missing) 3
 
< 0.1%

Length

2022-12-17T21:11:51.458049image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
35tzxthmbglbmjmz7fn1hj 4
 
< 0.1%
2a1imaowq5mnvlfbdv4qkf 3
 
< 0.1%
50fkjxrv0zltt9ehzgbop7 3
 
< 0.1%
5ljbzsaujvtuyvwjkthzii 2
 
< 0.1%
486qgevvkfhkwdpv9zmn5i 2
 
< 0.1%
0klzuinhomnqko1jvtmgce 2
 
< 0.1%
5uu1uuyauiboiiusqg7wl3 2
 
< 0.1%
4686eq81deswha90bcdlc9 2
 
< 0.1%
08mxslupaeus9xfblx0add 2
 
< 0.1%
3qysxz7h7nisi8wausdcbr 2
 
< 0.1%
Other values (8614) 8713
99.7%

Most occurring characters

ValueCountFrequency (%)
3 4203
 
2.2%
1 4175
 
2.2%
4 4117
 
2.1%
0 4083
 
2.1%
2 4076
 
2.1%
6 4065
 
2.1%
5 3996
 
2.1%
7 3905
 
2.0%
p 3052
 
1.6%
L 3046
 
1.6%
Other values (52) 153496
79.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 77174
40.2%
Uppercase Letter 76526
39.8%
Decimal Number 38514
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p 3052
 
4.0%
i 3034
 
3.9%
c 3020
 
3.9%
y 3019
 
3.9%
b 3016
 
3.9%
z 3014
 
3.9%
t 3007
 
3.9%
x 2997
 
3.9%
j 2992
 
3.9%
s 2986
 
3.9%
Other values (16) 47037
60.9%
Uppercase Letter
ValueCountFrequency (%)
L 3046
 
4.0%
F 3031
 
4.0%
P 3030
 
4.0%
U 2992
 
3.9%
Z 2987
 
3.9%
D 2976
 
3.9%
Y 2974
 
3.9%
H 2974
 
3.9%
B 2969
 
3.9%
M 2963
 
3.9%
Other values (16) 46584
60.9%
Decimal Number
ValueCountFrequency (%)
3 4203
10.9%
1 4175
10.8%
4 4117
10.7%
0 4083
10.6%
2 4076
10.6%
6 4065
10.6%
5 3996
10.4%
7 3905
10.1%
9 3013
7.8%
8 2881
7.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 153700
80.0%
Common 38514
 
20.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
p 3052
 
2.0%
L 3046
 
2.0%
i 3034
 
2.0%
F 3031
 
2.0%
P 3030
 
2.0%
c 3020
 
2.0%
y 3019
 
2.0%
b 3016
 
2.0%
z 3014
 
2.0%
t 3007
 
2.0%
Other values (42) 123431
80.3%
Common
ValueCountFrequency (%)
3 4203
10.9%
1 4175
10.8%
4 4117
10.7%
0 4083
10.6%
2 4076
10.6%
6 4065
10.6%
5 3996
10.4%
7 3905
10.1%
9 3013
7.8%
8 2881
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 192214
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 4203
 
2.2%
1 4175
 
2.2%
4 4117
 
2.1%
0 4083
 
2.1%
2 4076
 
2.1%
6 4065
 
2.1%
5 3996
 
2.1%
7 3905
 
2.0%
p 3052
 
1.6%
L 3046
 
1.6%
Other values (52) 153496
79.9%

uri
Categorical

HIGH CARDINALITY
UNIFORM

Distinct8624
Distinct (%)98.7%
Missing3
Missing (%)< 0.1%
Memory size861.9 KiB
spotify:track:35tzxthMBglBMjmZ7Fn1hj
 
4
spotify:track:50fkJxrv0ZLTt9EHZGBOP7
 
3
spotify:track:2a1iMaoWQ5MnvLFBDv4qkf
 
3
spotify:track:7CBfP01G2qOB9GFWAN7ocL
 
2
spotify:track:5hChuUMe5iHfjkXgzj1D4b
 
2
Other values (8619)
8723 

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters314532
Distinct characters63
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8515 ?
Unique (%)97.5%

Sample

1st rowspotify:track:4knd2gQyr2DTRLfJDHcyMS
2nd rowspotify:track:7ns3vcnzAxjCZVYwlwazah
3rd rowspotify:track:2S8gTIectkC846PHdsAshC
4th rowspotify:track:5i5fCpsnqDJ9AfeObgd0gW
5th rowspotify:track:7qDUOLnOLYKTwzvCJDnYRf

Common Values

ValueCountFrequency (%)
spotify:track:35tzxthMBglBMjmZ7Fn1hj 4
 
< 0.1%
spotify:track:50fkJxrv0ZLTt9EHZGBOP7 3
 
< 0.1%
spotify:track:2a1iMaoWQ5MnvLFBDv4qkf 3
 
< 0.1%
spotify:track:7CBfP01G2qOB9GFWAN7ocL 2
 
< 0.1%
spotify:track:5hChuUMe5iHfjkXgzj1D4b 2
 
< 0.1%
spotify:track:4JSo7H334HZiZtDIUcMpv8 2
 
< 0.1%
spotify:track:7tDEl7uRnoGdi0SFghDCz6 2
 
< 0.1%
spotify:track:4OUmlC67FoPLvQNuE5C7kF 2
 
< 0.1%
spotify:track:0GDz2tPdh6FbxJxM75SQB4 2
 
< 0.1%
spotify:track:4I5wK1FPimEXQb2zID7Duy 2
 
< 0.1%
Other values (8614) 8713
99.7%
(Missing) 3
 
< 0.1%

Length

2022-12-17T21:11:51.833733image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
spotify:track:35tzxthmbglbmjmz7fn1hj 4
 
< 0.1%
spotify:track:2a1imaowq5mnvlfbdv4qkf 3
 
< 0.1%
spotify:track:50fkjxrv0zltt9ehzgbop7 3
 
< 0.1%
spotify:track:5ljbzsaujvtuyvwjkthzii 2
 
< 0.1%
spotify:track:486qgevvkfhkwdpv9zmn5i 2
 
< 0.1%
spotify:track:0klzuinhomnqko1jvtmgce 2
 
< 0.1%
spotify:track:5uu1uuyauiboiiusqg7wl3 2
 
< 0.1%
spotify:track:4686eq81deswha90bcdlc9 2
 
< 0.1%
spotify:track:08mxslupaeus9xfblx0add 2
 
< 0.1%
spotify:track:3qysxz7h7nisi8wausdcbr 2
 
< 0.1%
Other values (8614) 8713
99.7%

Most occurring characters

ValueCountFrequency (%)
t 20481
 
6.5%
: 17474
 
5.6%
p 11789
 
3.7%
i 11771
 
3.7%
c 11757
 
3.7%
y 11756
 
3.7%
s 11723
 
3.7%
f 11712
 
3.7%
k 11693
 
3.7%
o 11688
 
3.7%
Other values (53) 182688
58.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 182018
57.9%
Uppercase Letter 76526
24.3%
Decimal Number 38514
 
12.2%
Other Punctuation 17474
 
5.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 20481
 
11.3%
p 11789
 
6.5%
i 11771
 
6.5%
c 11757
 
6.5%
y 11756
 
6.5%
s 11723
 
6.4%
f 11712
 
6.4%
k 11693
 
6.4%
o 11688
 
6.4%
a 11675
 
6.4%
Other values (16) 55973
30.8%
Uppercase Letter
ValueCountFrequency (%)
L 3046
 
4.0%
F 3031
 
4.0%
P 3030
 
4.0%
U 2992
 
3.9%
Z 2987
 
3.9%
D 2976
 
3.9%
H 2974
 
3.9%
Y 2974
 
3.9%
B 2969
 
3.9%
M 2963
 
3.9%
Other values (16) 46584
60.9%
Decimal Number
ValueCountFrequency (%)
3 4203
10.9%
1 4175
10.8%
4 4117
10.7%
0 4083
10.6%
2 4076
10.6%
6 4065
10.6%
5 3996
10.4%
7 3905
10.1%
9 3013
7.8%
8 2881
7.5%
Other Punctuation
ValueCountFrequency (%)
: 17474
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 258544
82.2%
Common 55988
 
17.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 20481
 
7.9%
p 11789
 
4.6%
i 11771
 
4.6%
c 11757
 
4.5%
y 11756
 
4.5%
s 11723
 
4.5%
f 11712
 
4.5%
k 11693
 
4.5%
o 11688
 
4.5%
a 11675
 
4.5%
Other values (42) 132499
51.2%
Common
ValueCountFrequency (%)
: 17474
31.2%
3 4203
 
7.5%
1 4175
 
7.5%
4 4117
 
7.4%
0 4083
 
7.3%
2 4076
 
7.3%
6 4065
 
7.3%
5 3996
 
7.1%
7 3905
 
7.0%
9 3013
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 314532
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 20481
 
6.5%
: 17474
 
5.6%
p 11789
 
3.7%
i 11771
 
3.7%
c 11757
 
3.7%
y 11756
 
3.7%
s 11723
 
3.7%
f 11712
 
3.7%
k 11693
 
3.7%
o 11688
 
3.7%
Other values (53) 182688
58.1%
Distinct8624
Distinct (%)98.7%
Missing3
Missing (%)< 0.1%
Memory size1.0 MiB
https://api.spotify.com/v1/tracks/35tzxthMBglBMjmZ7Fn1hj
 
4
https://api.spotify.com/v1/tracks/50fkJxrv0ZLTt9EHZGBOP7
 
3
https://api.spotify.com/v1/tracks/2a1iMaoWQ5MnvLFBDv4qkf
 
3
https://api.spotify.com/v1/tracks/7CBfP01G2qOB9GFWAN7ocL
 
2
https://api.spotify.com/v1/tracks/5hChuUMe5iHfjkXgzj1D4b
 
2
Other values (8619)
8723 
(Missing)
 
3
ValueCountFrequency (%)
https://api.spotify.com/v1/tracks/35tzxthMBglBMjmZ7Fn1hj 4
 
< 0.1%
https://api.spotify.com/v1/tracks/50fkJxrv0ZLTt9EHZGBOP7 3
 
< 0.1%
https://api.spotify.com/v1/tracks/2a1iMaoWQ5MnvLFBDv4qkf 3
 
< 0.1%
https://api.spotify.com/v1/tracks/7CBfP01G2qOB9GFWAN7ocL 2
 
< 0.1%
https://api.spotify.com/v1/tracks/5hChuUMe5iHfjkXgzj1D4b 2
 
< 0.1%
https://api.spotify.com/v1/tracks/4JSo7H334HZiZtDIUcMpv8 2
 
< 0.1%
https://api.spotify.com/v1/tracks/7tDEl7uRnoGdi0SFghDCz6 2
 
< 0.1%
https://api.spotify.com/v1/tracks/4OUmlC67FoPLvQNuE5C7kF 2
 
< 0.1%
https://api.spotify.com/v1/tracks/0GDz2tPdh6FbxJxM75SQB4 2
 
< 0.1%
https://api.spotify.com/v1/tracks/4I5wK1FPimEXQb2zID7Duy 2
 
< 0.1%
Other values (8614) 8713
99.7%
(Missing) 3
 
< 0.1%
ValueCountFrequency (%)
https 8737
> 99.9%
(Missing) 3
 
< 0.1%
ValueCountFrequency (%)
api.spotify.com 8737
> 99.9%
(Missing) 3
 
< 0.1%
ValueCountFrequency (%)
/v1/tracks/35tzxthMBglBMjmZ7Fn1hj 4
 
< 0.1%
/v1/tracks/2a1iMaoWQ5MnvLFBDv4qkf 3
 
< 0.1%
/v1/tracks/50fkJxrv0ZLTt9EHZGBOP7 3
 
< 0.1%
/v1/tracks/1AffwMT4CUYidGHAFiW4vm 2
 
< 0.1%
/v1/tracks/2Foc5Q5nqNiosCNqttzHof 2
 
< 0.1%
/v1/tracks/44IViv0bVWrEmScLcFssav 2
 
< 0.1%
/v1/tracks/75ZwuzmGArceowMVQDEB1J 2
 
< 0.1%
/v1/tracks/7lW6kGMERd2RNwJGbrJFiC 2
 
< 0.1%
/v1/tracks/10FLYqpqDN4uo6eWtD6WEB 2
 
< 0.1%
/v1/tracks/5roGmmhLjfb2WqukL1u0TV 2
 
< 0.1%
Other values (8614) 8713
99.7%
(Missing) 3
 
< 0.1%
ValueCountFrequency (%)
8737
> 99.9%
(Missing) 3
 
< 0.1%
ValueCountFrequency (%)
8737
> 99.9%
(Missing) 3
 
< 0.1%
Distinct8624
Distinct (%)98.7%
Missing3
Missing (%)< 0.1%
Memory size1.1 MiB
https://api.spotify.com/v1/audio-analysis/35tzxthMBglBMjmZ7Fn1hj
 
4
https://api.spotify.com/v1/audio-analysis/50fkJxrv0ZLTt9EHZGBOP7
 
3
https://api.spotify.com/v1/audio-analysis/2a1iMaoWQ5MnvLFBDv4qkf
 
3
https://api.spotify.com/v1/audio-analysis/7CBfP01G2qOB9GFWAN7ocL
 
2
https://api.spotify.com/v1/audio-analysis/5hChuUMe5iHfjkXgzj1D4b
 
2
Other values (8619)
8723 
(Missing)
 
3
ValueCountFrequency (%)
https://api.spotify.com/v1/audio-analysis/35tzxthMBglBMjmZ7Fn1hj 4
 
< 0.1%
https://api.spotify.com/v1/audio-analysis/50fkJxrv0ZLTt9EHZGBOP7 3
 
< 0.1%
https://api.spotify.com/v1/audio-analysis/2a1iMaoWQ5MnvLFBDv4qkf 3
 
< 0.1%
https://api.spotify.com/v1/audio-analysis/7CBfP01G2qOB9GFWAN7ocL 2
 
< 0.1%
https://api.spotify.com/v1/audio-analysis/5hChuUMe5iHfjkXgzj1D4b 2
 
< 0.1%
https://api.spotify.com/v1/audio-analysis/4JSo7H334HZiZtDIUcMpv8 2
 
< 0.1%
https://api.spotify.com/v1/audio-analysis/7tDEl7uRnoGdi0SFghDCz6 2
 
< 0.1%
https://api.spotify.com/v1/audio-analysis/4OUmlC67FoPLvQNuE5C7kF 2
 
< 0.1%
https://api.spotify.com/v1/audio-analysis/0GDz2tPdh6FbxJxM75SQB4 2
 
< 0.1%
https://api.spotify.com/v1/audio-analysis/4I5wK1FPimEXQb2zID7Duy 2
 
< 0.1%
Other values (8614) 8713
99.7%
(Missing) 3
 
< 0.1%
ValueCountFrequency (%)
https 8737
> 99.9%
(Missing) 3
 
< 0.1%
ValueCountFrequency (%)
api.spotify.com 8737
> 99.9%
(Missing) 3
 
< 0.1%
ValueCountFrequency (%)
/v1/audio-analysis/35tzxthMBglBMjmZ7Fn1hj 4
 
< 0.1%
/v1/audio-analysis/2a1iMaoWQ5MnvLFBDv4qkf 3
 
< 0.1%
/v1/audio-analysis/50fkJxrv0ZLTt9EHZGBOP7 3
 
< 0.1%
/v1/audio-analysis/1AffwMT4CUYidGHAFiW4vm 2
 
< 0.1%
/v1/audio-analysis/2Foc5Q5nqNiosCNqttzHof 2
 
< 0.1%
/v1/audio-analysis/44IViv0bVWrEmScLcFssav 2
 
< 0.1%
/v1/audio-analysis/75ZwuzmGArceowMVQDEB1J 2
 
< 0.1%
/v1/audio-analysis/7lW6kGMERd2RNwJGbrJFiC 2
 
< 0.1%
/v1/audio-analysis/10FLYqpqDN4uo6eWtD6WEB 2
 
< 0.1%
/v1/audio-analysis/5roGmmhLjfb2WqukL1u0TV 2
 
< 0.1%
Other values (8614) 8713
99.7%
(Missing) 3
 
< 0.1%
ValueCountFrequency (%)
8737
> 99.9%
(Missing) 3
 
< 0.1%
ValueCountFrequency (%)
8737
> 99.9%
(Missing) 3
 
< 0.1%

duration_ms
Real number (ℝ)

Distinct7894
Distinct (%)90.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean233982.53
Minimum15106
Maximum1172433
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size136.6 KiB
2022-12-17T21:11:52.329681image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum15106
5-th percentile131862
Q1185468.5
median220704
Q3267370.75
95-th percentile376693.7
Maximum1172433
Range1157327
Interquartile range (IQR)81902.25

Descriptive statistics

Standard deviation83531.414
Coefficient of variation (CV)0.35699851
Kurtosis13.641048
Mean233982.53
Median Absolute Deviation (MAD)39066.5
Skewness2.2297306
Sum2.0450073 × 109
Variance6.9774972 × 109
MonotonicityNot monotonic
2022-12-17T21:11:52.935701image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
192000 8
 
0.1%
180000 8
 
0.1%
174000 7
 
0.1%
200000 6
 
0.1%
228000 6
 
0.1%
204000 5
 
0.1%
213000 5
 
0.1%
196000 5
 
0.1%
160000 5
 
0.1%
186000 5
 
0.1%
Other values (7884) 8680
99.3%
ValueCountFrequency (%)
15106 1
< 0.1%
16545 1
< 0.1%
24054 1
< 0.1%
25575 1
< 0.1%
27293 1
< 0.1%
28813 1
< 0.1%
29754 1
< 0.1%
30000 1
< 0.1%
30373 1
< 0.1%
30826 1
< 0.1%
ValueCountFrequency (%)
1172433 1
< 0.1%
1142213 1
< 0.1%
1074213 1
< 0.1%
1059733 1
< 0.1%
1045533 1
< 0.1%
970464 1
< 0.1%
969864 1
< 0.1%
950210 1
< 0.1%
936739 1
< 0.1%
902000 1
< 0.1%

time_signature
Categorical

Distinct5
Distinct (%)0.1%
Missing3
Missing (%)< 0.1%
Memory size580.3 KiB
4.0
7939 
3.0
 
567
5.0
 
155
1.0
 
75
0.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters26211
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row4.0
2nd row3.0
3rd row4.0
4th row4.0
5th row4.0

Common Values

ValueCountFrequency (%)
4.0 7939
90.8%
3.0 567
 
6.5%
5.0 155
 
1.8%
1.0 75
 
0.9%
0.0 1
 
< 0.1%
(Missing) 3
 
< 0.1%

Length

2022-12-17T21:11:53.527981image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-17T21:11:54.004074image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
4.0 7939
90.9%
3.0 567
 
6.5%
5.0 155
 
1.8%
1.0 75
 
0.9%
0.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 8738
33.3%
. 8737
33.3%
4 7939
30.3%
3 567
 
2.2%
5 155
 
0.6%
1 75
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17474
66.7%
Other Punctuation 8737
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8738
50.0%
4 7939
45.4%
3 567
 
3.2%
5 155
 
0.9%
1 75
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 8737
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 26211
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8738
33.3%
. 8737
33.3%
4 7939
30.3%
3 567
 
2.2%
5 155
 
0.6%
1 75
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26211
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8738
33.3%
. 8737
33.3%
4 7939
30.3%
3 567
 
2.2%
5 155
 
0.6%
1 75
 
0.3%

id_hash
Categorical

HIGH CARDINALITY
UNIFORM

Distinct8737
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
23134ee7b28d5abec1f2cf07b690bc8a74f8c1a5b18ef42abaf1b256990c8293
 
4
40826686141ecbeb2a9a032ba9b17ae9966d3b9285f651017bb33d4ed988f717
 
1
12b4a008b377429b7bb86e1fd98d740b7f40f2c3aea7e5f261315180c9205aa5
 
1
c98b40e2b7cc30c1a38fe426e461a343c47bc18650d30734932ada93bb9b4219
 
1
d33f1a814879dc4b2cb79d8a11cdae67bcefa65b3102e4be1db7adee9cd0e667
 
1
Other values (8732)
8732 

Length

Max length64
Median length64
Mean length64
Min length64

Characters and Unicode

Total characters559360
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8736 ?
Unique (%)> 99.9%

Sample

1st row40826686141ecbeb2a9a032ba9b17ae9966d3b9285f651017bb33d4ed988f717
2nd row8fe126c7d28ac465eacb644ec878ea554744d6dd7d2fef696df6e847bd133694
3rd row5139a49bdfa4749b67c074870911e75976d58b32b076d1d7a72f4813edfe76a3
4th row2b34ac0f1ca8fac70845b6cb894bac839ab229454203ef29b3d2bee9058fd560
5th row283330c524139861dda440b4ea23424f6930f9b900372737d91e20213fe1c6a4

Common Values

ValueCountFrequency (%)
23134ee7b28d5abec1f2cf07b690bc8a74f8c1a5b18ef42abaf1b256990c8293 4
 
< 0.1%
40826686141ecbeb2a9a032ba9b17ae9966d3b9285f651017bb33d4ed988f717 1
 
< 0.1%
12b4a008b377429b7bb86e1fd98d740b7f40f2c3aea7e5f261315180c9205aa5 1
 
< 0.1%
c98b40e2b7cc30c1a38fe426e461a343c47bc18650d30734932ada93bb9b4219 1
 
< 0.1%
d33f1a814879dc4b2cb79d8a11cdae67bcefa65b3102e4be1db7adee9cd0e667 1
 
< 0.1%
93bec58465fb2885a807f83ded2789c965ffeff20d0b493d64b796bffb7ea65c 1
 
< 0.1%
69ec705935becbd3f8f351005fba740c37fac76f2143bacbe965d25770512717 1
 
< 0.1%
bafd917c861839e4d430a9453dc52d053a482e1e2b773cefd4fbb0c2cc3dcf4b 1
 
< 0.1%
f8e84ed5ff8cbaaaaa63b2145b553547ee92ca0424250b81fe82f4940a9786b5 1
 
< 0.1%
05467790cdb77fda2459018283ab271ae1fe16ab87b7038c77928caef806a553 1
 
< 0.1%
Other values (8727) 8727
99.9%

Length

2022-12-17T21:11:54.371641image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
23134ee7b28d5abec1f2cf07b690bc8a74f8c1a5b18ef42abaf1b256990c8293 4
 
< 0.1%
118e08bd62d0adc42bb4ca154793d8f464f408b3c20e581606ea2c8862d8eff8 1
 
< 0.1%
72978a16871bc08ad3d64b24fd6eca05c8d50a08bd8cd7978ac238e1289134eb 1
 
< 0.1%
352e3c20633bbb0fb9932f683092f9470cb3bb0b4a9962ef6b022702e609efaa 1
 
< 0.1%
4fc625da9ae169ec64d1c0f922d6d1ac8fa660b06408b0e868960cf2ef16a279 1
 
< 0.1%
94f029a0618f5ecf11ee990cb8aa96e33f661dc338dc3133e8923007bd9bd104 1
 
< 0.1%
c1e86348af4afbe52e38485e25197ba3b262717e038656f413771f97f0a68e19 1
 
< 0.1%
4c77ed4fabdd9a9967078bd46a7abc6b6cf72a632245c58ddab554961b5f2d98 1
 
< 0.1%
cbe55ee5f173ea3090ec1a5bac0b7829b32295e7f869103c13068034843e2c85 1
 
< 0.1%
a35fcb9b76c46fb421ceb6a1a48e20af460b3bb6cf43627b262333e354409984 1
 
< 0.1%
Other values (8727) 8727
99.9%

Most occurring characters

ValueCountFrequency (%)
7 35274
 
6.3%
b 35149
 
6.3%
2 35093
 
6.3%
f 35051
 
6.3%
5 35030
 
6.3%
c 35019
 
6.3%
3 35008
 
6.3%
1 34984
 
6.3%
8 34982
 
6.3%
4 34921
 
6.2%
Other values (6) 208849
37.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 349574
62.5%
Lowercase Letter 209786
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 35274
10.1%
2 35093
10.0%
5 35030
10.0%
3 35008
10.0%
1 34984
10.0%
8 34982
10.0%
4 34921
10.0%
9 34800
10.0%
0 34750
9.9%
6 34732
9.9%
Lowercase Letter
ValueCountFrequency (%)
b 35149
16.8%
f 35051
16.7%
c 35019
16.7%
e 34908
16.6%
a 34890
16.6%
d 34769
16.6%

Most occurring scripts

ValueCountFrequency (%)
Common 349574
62.5%
Latin 209786
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
7 35274
10.1%
2 35093
10.0%
5 35030
10.0%
3 35008
10.0%
1 34984
10.0%
8 34982
10.0%
4 34921
10.0%
9 34800
10.0%
0 34750
9.9%
6 34732
9.9%
Latin
ValueCountFrequency (%)
b 35149
16.8%
f 35051
16.7%
c 35019
16.7%
e 34908
16.6%
a 34890
16.6%
d 34769
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 559360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 35274
 
6.3%
b 35149
 
6.3%
2 35093
 
6.3%
f 35051
 
6.3%
5 35030
 
6.3%
c 35019
 
6.3%
3 35008
 
6.3%
1 34984
 
6.3%
8 34982
 
6.3%
4 34921
 
6.2%
Other values (6) 208849
37.3%

album
Categorical

Distinct5378
Distinct (%)61.5%
Missing0
Missing (%)0.0%
Memory size717.2 KiB
Maschinen wie ich (Ungekürzt)
 
39
Inside In, Inside Out (15th Anniversary Deluxe)
 
21
Past Cloaks
 
19
The OOZ
 
19
Definitely Maybe (Deluxe Edition Remastered)
 
18
Other values (5373)
8624 

Length

Max length169
Median length93
Mean length17.003776
Min length1

Characters and Unicode

Total characters148613
Distinct characters590
Distinct categories20 ?
Distinct scripts13 ?
Distinct blocks14 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4283 ?
Unique (%)49.0%

Sample

1st rowI'm Sick of This/So We Can Fuck
2nd row871
3rd rowSubstance (We Woke Up)
4th rowGuilty Conscience (Tame Impala Remix)
5th rowGuilty Conscience (Tame Impala Remix)

Common Values

ValueCountFrequency (%)
Maschinen wie ich (Ungekürzt) 39
 
0.4%
Inside In, Inside Out (15th Anniversary Deluxe) 21
 
0.2%
Past Cloaks 19
 
0.2%
The OOZ 19
 
0.2%
Definitely Maybe (Deluxe Edition Remastered) 18
 
0.2%
Back to Mine: Jungle (DJ Mix) 18
 
0.2%
Light Up Gold + Tally All The Things That You Broke 18
 
0.2%
Is the Is Are 17
 
0.2%
A Cross The Universe 17
 
0.2%
Justice 17
 
0.2%
Other values (5368) 8537
97.7%

Length

2022-12-17T21:11:54.911401image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 1018
 
3.9%
365
 
1.4%
of 326
 
1.3%
a 303
 
1.2%
in 274
 
1.1%
deluxe 263
 
1.0%
you 235
 
0.9%
to 216
 
0.8%
i 184
 
0.7%
for 169
 
0.7%
Other values (6334) 22445
87.0%

Most occurring characters

ValueCountFrequency (%)
17058
 
11.5%
e 14078
 
9.5%
o 8252
 
5.6%
i 7936
 
5.3%
n 7872
 
5.3%
a 7864
 
5.3%
r 6996
 
4.7%
t 6457
 
4.3%
s 5981
 
4.0%
l 4942
 
3.3%
Other values (580) 61177
41.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 98726
66.4%
Uppercase Letter 26038
 
17.5%
Space Separator 17058
 
11.5%
Other Punctuation 1919
 
1.3%
Decimal Number 1416
 
1.0%
Open Punctuation 1192
 
0.8%
Close Punctuation 1192
 
0.8%
Other Letter 651
 
0.4%
Dash Punctuation 264
 
0.2%
Final Punctuation 43
 
< 0.1%
Other values (10) 114
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
ا 33
 
5.1%
ل 24
 
3.7%
م 20
 
3.1%
ي 19
 
2.9%
י 11
 
1.7%
و 10
 
1.5%
10
 
1.5%
ر 9
 
1.4%
ה 9
 
1.4%
ל 8
 
1.2%
Other values (328) 498
76.5%
Lowercase Letter
ValueCountFrequency (%)
e 14078
14.3%
o 8252
 
8.4%
i 7936
 
8.0%
n 7872
 
8.0%
a 7864
 
8.0%
r 6996
 
7.1%
t 6457
 
6.5%
s 5981
 
6.1%
l 4942
 
5.0%
u 3602
 
3.6%
Other values (107) 24746
25.1%
Uppercase Letter
ValueCountFrequency (%)
S 2199
 
8.4%
T 2136
 
8.2%
A 1655
 
6.4%
M 1592
 
6.1%
D 1487
 
5.7%
I 1356
 
5.2%
R 1299
 
5.0%
C 1279
 
4.9%
L 1278
 
4.9%
B 1275
 
4.9%
Other values (55) 10482
40.3%
Other Punctuation
ValueCountFrequency (%)
. 526
27.4%
' 404
21.1%
, 308
16.1%
: 174
 
9.1%
/ 150
 
7.8%
& 124
 
6.5%
! 110
 
5.7%
? 63
 
3.3%
" 27
 
1.4%
# 11
 
0.6%
Other values (6) 22
 
1.1%
Decimal Number
ValueCountFrequency (%)
1 314
22.2%
0 302
21.3%
2 291
20.6%
9 125
 
8.8%
5 75
 
5.3%
6 74
 
5.2%
4 65
 
4.6%
3 62
 
4.4%
8 55
 
3.9%
7 53
 
3.7%
Nonspacing Mark
ValueCountFrequency (%)
3
15.0%
3
15.0%
́ 2
10.0%
̊ 2
10.0%
2
10.0%
2
10.0%
2
10.0%
2
10.0%
1
 
5.0%
1
 
5.0%
Math Symbol
ValueCountFrequency (%)
+ 22
53.7%
~ 8
 
19.5%
> 3
 
7.3%
= 2
 
4.9%
× 2
 
4.9%
< 2
 
4.9%
÷ 1
 
2.4%
| 1
 
2.4%
Open Punctuation
ValueCountFrequency (%)
( 1139
95.6%
[ 51
 
4.3%
1
 
0.1%
1
 
0.1%
Close Punctuation
ValueCountFrequency (%)
) 1139
95.6%
] 51
 
4.3%
1
 
0.1%
1
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 259
98.1%
4
 
1.5%
1
 
0.4%
Spacing Mark
ValueCountFrequency (%)
ि 2
50.0%
1
25.0%
1
25.0%
Final Punctuation
ValueCountFrequency (%)
42
97.7%
1
 
2.3%
Currency Symbol
ValueCountFrequency (%)
4
57.1%
$ 3
42.9%
Initial Punctuation
ValueCountFrequency (%)
1
50.0%
1
50.0%
Space Separator
ValueCountFrequency (%)
17058
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 31
100.0%
Modifier Letter
ValueCountFrequency (%)
3
100.0%
Other Symbol
ValueCountFrequency (%)
® 2
100.0%
Format
ValueCountFrequency (%)
2
100.0%
Modifier Symbol
ValueCountFrequency (%)
^ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 124417
83.7%
Common 23174
 
15.6%
Cyrillic 317
 
0.2%
Han 224
 
0.2%
Arabic 187
 
0.1%
Hebrew 86
 
0.1%
Hangul 77
 
0.1%
Katakana 37
 
< 0.1%
Greek 30
 
< 0.1%
Devanagari 27
 
< 0.1%
Other values (3) 37
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
10
 
4.5%
6
 
2.7%
4
 
1.8%
3
 
1.3%
3
 
1.3%
3
 
1.3%
3
 
1.3%
3
 
1.3%
3
 
1.3%
3
 
1.3%
Other values (158) 183
81.7%
Latin
ValueCountFrequency (%)
e 14078
 
11.3%
o 8252
 
6.6%
i 7936
 
6.4%
n 7872
 
6.3%
a 7864
 
6.3%
r 6996
 
5.6%
t 6457
 
5.2%
s 5981
 
4.8%
l 4942
 
4.0%
u 3602
 
2.9%
Other values (107) 50437
40.5%
Hangul
ValueCountFrequency (%)
3
 
3.9%
3
 
3.9%
3
 
3.9%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
굿 1
 
1.3%
1
 
1.3%
Other values (56) 56
72.7%
Common
ValueCountFrequency (%)
17058
73.6%
( 1139
 
4.9%
) 1139
 
4.9%
. 526
 
2.3%
' 404
 
1.7%
1 314
 
1.4%
, 308
 
1.3%
0 302
 
1.3%
2 291
 
1.3%
- 259
 
1.1%
Other values (47) 1434
 
6.2%
Cyrillic
ValueCountFrequency (%)
а 28
 
8.8%
с 27
 
8.5%
е 26
 
8.2%
н 25
 
7.9%
о 22
 
6.9%
и 21
 
6.6%
т 20
 
6.3%
р 14
 
4.4%
в 13
 
4.1%
л 12
 
3.8%
Other values (38) 109
34.4%
Arabic
ValueCountFrequency (%)
ا 33
17.6%
ل 24
12.8%
م 20
10.7%
ي 19
10.2%
و 10
 
5.3%
ر 9
 
4.8%
ن 8
 
4.3%
ع 7
 
3.7%
ق 6
 
3.2%
د 6
 
3.2%
Other values (16) 45
24.1%
Katakana
ValueCountFrequency (%)
5
 
13.5%
2
 
5.4%
2
 
5.4%
2
 
5.4%
2
 
5.4%
2
 
5.4%
2
 
5.4%
2
 
5.4%
2
 
5.4%
2
 
5.4%
Other values (14) 14
37.8%
Hebrew
ValueCountFrequency (%)
י 11
12.8%
ה 9
10.5%
ל 8
 
9.3%
ו 8
 
9.3%
א 7
 
8.1%
ם 6
 
7.0%
ח 5
 
5.8%
ש 5
 
5.8%
נ 3
 
3.5%
ג 3
 
3.5%
Other values (13) 21
24.4%
Thai
ValueCountFrequency (%)
3
 
11.1%
2
 
7.4%
2
 
7.4%
2
 
7.4%
2
 
7.4%
2
 
7.4%
1
 
3.7%
1
 
3.7%
1
 
3.7%
1
 
3.7%
Other values (10) 10
37.0%
Greek
ValueCountFrequency (%)
α 4
13.3%
ι 4
13.3%
ρ 3
10.0%
ε 2
 
6.7%
θ 2
 
6.7%
ο 2
 
6.7%
η 2
 
6.7%
ν 2
 
6.7%
μ 1
 
3.3%
ί 1
 
3.3%
Other values (7) 7
23.3%
Devanagari
ValueCountFrequency (%)
4
14.8%
3
11.1%
2
 
7.4%
2
 
7.4%
2
 
7.4%
2
 
7.4%
ि 2
 
7.4%
1
 
3.7%
1
 
3.7%
1
 
3.7%
Other values (7) 7
25.9%
Hiragana
ValueCountFrequency (%)
2
33.3%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
Inherited
ValueCountFrequency (%)
́ 2
50.0%
̊ 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 146936
98.9%
None 619
 
0.4%
Cyrillic 317
 
0.2%
CJK 224
 
0.2%
Arabic 187
 
0.1%
Hebrew 86
 
0.1%
Hangul 77
 
0.1%
Punctuation 55
 
< 0.1%
Katakana 44
 
< 0.1%
Devanagari 27
 
< 0.1%
Other values (4) 41
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
17058
 
11.6%
e 14078
 
9.6%
o 8252
 
5.6%
i 7936
 
5.4%
n 7872
 
5.4%
a 7864
 
5.4%
r 6996
 
4.8%
t 6457
 
4.4%
s 5981
 
4.1%
l 4942
 
3.4%
Other values (80) 59500
40.5%
None
ValueCountFrequency (%)
ü 165
26.7%
é 75
 
12.1%
ä 59
 
9.5%
ö 30
 
4.8%
ç 28
 
4.5%
Ç 14
 
2.3%
ğ 14
 
2.3%
ø 13
 
2.1%
è 12
 
1.9%
å 12
 
1.9%
Other values (80) 197
31.8%
Punctuation
ValueCountFrequency (%)
42
76.4%
4
 
7.3%
3
 
5.5%
2
 
3.6%
1
 
1.8%
1
 
1.8%
1
 
1.8%
1
 
1.8%
Arabic
ValueCountFrequency (%)
ا 33
17.6%
ل 24
12.8%
م 20
10.7%
ي 19
10.2%
و 10
 
5.3%
ر 9
 
4.8%
ن 8
 
4.3%
ع 7
 
3.7%
ق 6
 
3.2%
د 6
 
3.2%
Other values (16) 45
24.1%
Cyrillic
ValueCountFrequency (%)
а 28
 
8.8%
с 27
 
8.5%
е 26
 
8.2%
н 25
 
7.9%
о 22
 
6.9%
и 21
 
6.6%
т 20
 
6.3%
р 14
 
4.4%
в 13
 
4.1%
л 12
 
3.8%
Other values (38) 109
34.4%
Hebrew
ValueCountFrequency (%)
י 11
12.8%
ה 9
10.5%
ל 8
 
9.3%
ו 8
 
9.3%
א 7
 
8.1%
ם 6
 
7.0%
ח 5
 
5.8%
ש 5
 
5.8%
נ 3
 
3.5%
ג 3
 
3.5%
Other values (13) 21
24.4%
CJK
ValueCountFrequency (%)
10
 
4.5%
6
 
2.7%
4
 
1.8%
3
 
1.3%
3
 
1.3%
3
 
1.3%
3
 
1.3%
3
 
1.3%
3
 
1.3%
3
 
1.3%
Other values (158) 183
81.7%
Katakana
ValueCountFrequency (%)
5
 
11.4%
4
 
9.1%
3
 
6.8%
2
 
4.5%
2
 
4.5%
2
 
4.5%
2
 
4.5%
2
 
4.5%
2
 
4.5%
2
 
4.5%
Other values (16) 18
40.9%
Currency Symbols
ValueCountFrequency (%)
4
100.0%
Devanagari
ValueCountFrequency (%)
4
14.8%
3
11.1%
2
 
7.4%
2
 
7.4%
2
 
7.4%
2
 
7.4%
ि 2
 
7.4%
1
 
3.7%
1
 
3.7%
1
 
3.7%
Other values (7) 7
25.9%
Hangul
ValueCountFrequency (%)
3
 
3.9%
3
 
3.9%
3
 
3.9%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
굿 1
 
1.3%
1
 
1.3%
Other values (56) 56
72.7%
Thai
ValueCountFrequency (%)
3
 
11.1%
2
 
7.4%
2
 
7.4%
2
 
7.4%
2
 
7.4%
2
 
7.4%
1
 
3.7%
1
 
3.7%
1
 
3.7%
1
 
3.7%
Other values (10) 10
37.0%
Hiragana
ValueCountFrequency (%)
2
33.3%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
Diacriticals
ValueCountFrequency (%)
́ 2
50.0%
̊ 2
50.0%

release_date
Categorical

Distinct2194
Distinct (%)25.1%
Missing0
Missing (%)0.0%
Memory size638.4 KiB
2020-02-28
 
47
2021-03-26
 
45
2021-08-27
 
43
2019-09-13
 
43
2006-01-01
 
42
Other values (2189)
8520 

Length

Max length10
Median length10
Mean length9.7923341
Min length4

Characters and Unicode

Total characters85585
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1095 ?
Unique (%)12.5%

Sample

1st row2020-05-01
2nd row2020-12-25
3rd row2021-02-03
4th row2020-07-24
5th row2020-07-24

Common Values

ValueCountFrequency (%)
2020-02-28 47
 
0.5%
2021-03-26 45
 
0.5%
2021-08-27 43
 
0.5%
2019-09-13 43
 
0.5%
2006-01-01 42
 
0.5%
2021-01-15 41
 
0.5%
2019-10-18 41
 
0.5%
2021-02-26 41
 
0.5%
2019-06-26 39
 
0.4%
2021-04-30 38
 
0.4%
Other values (2184) 8320
95.2%

Length

2022-12-17T21:11:55.453030image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-02-28 47
 
0.5%
2021-03-26 45
 
0.5%
2021-08-27 43
 
0.5%
2019-09-13 43
 
0.5%
2006-01-01 42
 
0.5%
2021-01-15 41
 
0.5%
2019-10-18 41
 
0.5%
2021-02-26 41
 
0.5%
2019-06-26 39
 
0.4%
2021-04-30 38
 
0.4%
Other values (2184) 8320
95.2%

Most occurring characters

ValueCountFrequency (%)
0 21266
24.8%
- 16875
19.7%
2 16855
19.7%
1 13805
16.1%
9 3761
 
4.4%
8 2410
 
2.8%
3 2388
 
2.8%
6 2267
 
2.6%
5 2104
 
2.5%
7 1995
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68710
80.3%
Dash Punctuation 16875
 
19.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21266
31.0%
2 16855
24.5%
1 13805
20.1%
9 3761
 
5.5%
8 2410
 
3.5%
3 2388
 
3.5%
6 2267
 
3.3%
5 2104
 
3.1%
7 1995
 
2.9%
4 1859
 
2.7%
Dash Punctuation
ValueCountFrequency (%)
- 16875
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 85585
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21266
24.8%
- 16875
19.7%
2 16855
19.7%
1 13805
16.1%
9 3761
 
4.4%
8 2410
 
2.8%
3 2388
 
2.8%
6 2267
 
2.6%
5 2104
 
2.5%
7 1995
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 85585
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21266
24.8%
- 16875
19.7%
2 16855
19.7%
1 13805
16.1%
9 3761
 
4.4%
8 2410
 
2.8%
3 2388
 
2.8%
6 2267
 
2.6%
5 2104
 
2.5%
7 1995
 
2.3%

explicit
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size76.8 KiB
False
7805 
True
935 
ValueCountFrequency (%)
False 7805
89.3%
True 935
 
10.7%
2022-12-17T21:11:55.967299image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

popularity
Real number (ℝ)

Distinct94
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.802632
Minimum0
Maximum98
Zeros207
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size136.6 KiB
2022-12-17T21:11:56.372947image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q127
median39
Q351
95-th percentile70
Maximum98
Range98
Interquartile range (IQR)24

Descriptive statistics

Standard deviation18.710001
Coefficient of variation (CV)0.48218382
Kurtosis-0.24571817
Mean38.802632
Median Absolute Deviation (MAD)12
Skewness-0.012849184
Sum339135
Variance350.06414
MonotonicityNot monotonic
2022-12-17T21:11:56.868970image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36 233
 
2.7%
35 226
 
2.6%
40 222
 
2.5%
38 217
 
2.5%
39 215
 
2.5%
0 207
 
2.4%
43 207
 
2.4%
41 205
 
2.3%
44 201
 
2.3%
37 198
 
2.3%
Other values (84) 6609
75.6%
ValueCountFrequency (%)
0 207
2.4%
1 93
1.1%
2 47
 
0.5%
3 40
 
0.5%
4 54
 
0.6%
5 45
 
0.5%
6 41
 
0.5%
7 41
 
0.5%
8 52
 
0.6%
9 52
 
0.6%
ValueCountFrequency (%)
98 2
 
< 0.1%
96 1
 
< 0.1%
91 4
 
< 0.1%
90 2
 
< 0.1%
89 7
0.1%
88 4
 
< 0.1%
87 4
 
< 0.1%
86 9
0.1%
85 11
0.1%
84 14
0.2%

isrc
Categorical

HIGH CARDINALITY
UNIFORM

Distinct8626
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Memory size657.3 KiB
ITR007900051
 
4
GBAYE9400055
 
3
FR8EU1800030
 
3
GBBKS0900056
 
2
QM38F1700004
 
2
Other values (8621)
8726 

Length

Max length15
Median length12
Mean length12.005492
Min length12

Characters and Unicode

Total characters104928
Distinct characters55
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8516 ?
Unique (%)97.4%

Sample

1st rowGBBPW2000087
2nd rowGBXNG2015003
3rd rowUSLD91730821
4th rowUSUM72013244
5th rowUSUM72015141

Common Values

ValueCountFrequency (%)
ITR007900051 4
 
< 0.1%
GBAYE9400055 3
 
< 0.1%
FR8EU1800030 3
 
< 0.1%
GBBKS0900056 2
 
< 0.1%
QM38F1700004 2
 
< 0.1%
AUFF01700121 2
 
< 0.1%
US25X1088819 2
 
< 0.1%
GBUM72006758 2
 
< 0.1%
USUG12203266 2
 
< 0.1%
GBUM71903648 2
 
< 0.1%
Other values (8616) 8716
99.7%

Length

2022-12-17T21:11:57.276087image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
itr007900051 4
 
< 0.1%
fr8eu1800030 3
 
< 0.1%
gbaye9400055 3
 
< 0.1%
us3r42052401 2
 
< 0.1%
fr8eu1800110 2
 
< 0.1%
gbcvz0300858 2
 
< 0.1%
fr0nt0800980 2
 
< 0.1%
qmlld1500013 2
 
< 0.1%
qzhn62072414 2
 
< 0.1%
gbswl1700022 2
 
< 0.1%
Other values (8616) 8716
99.7%

Most occurring characters

ValueCountFrequency (%)
0 19021
18.1%
1 12123
 
11.6%
2 8013
 
7.6%
9 4612
 
4.4%
3 4486
 
4.3%
7 4367
 
4.2%
5 4270
 
4.1%
6 4188
 
4.0%
U 3992
 
3.8%
4 3977
 
3.8%
Other values (45) 35879
34.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69010
65.8%
Uppercase Letter 35631
34.0%
Lowercase Letter 239
 
0.2%
Dash Punctuation 48
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 3992
 
11.2%
S 3585
 
10.1%
B 3292
 
9.2%
G 2897
 
8.1%
E 2216
 
6.2%
M 1962
 
5.5%
A 1931
 
5.4%
Q 1491
 
4.2%
C 1453
 
4.1%
R 1317
 
3.7%
Other values (16) 11495
32.3%
Lowercase Letter
ValueCountFrequency (%)
s 53
22.2%
u 53
22.2%
h 28
11.7%
c 21
 
8.8%
g 20
 
8.4%
m 17
 
7.1%
d 8
 
3.3%
y 8
 
3.3%
j 8
 
3.3%
x 4
 
1.7%
Other values (8) 19
 
7.9%
Decimal Number
ValueCountFrequency (%)
0 19021
27.6%
1 12123
17.6%
2 8013
11.6%
9 4612
 
6.7%
3 4486
 
6.5%
7 4367
 
6.3%
5 4270
 
6.2%
6 4188
 
6.1%
4 3977
 
5.8%
8 3953
 
5.7%
Dash Punctuation
ValueCountFrequency (%)
- 48
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69058
65.8%
Latin 35870
34.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 3992
 
11.1%
S 3585
 
10.0%
B 3292
 
9.2%
G 2897
 
8.1%
E 2216
 
6.2%
M 1962
 
5.5%
A 1931
 
5.4%
Q 1491
 
4.2%
C 1453
 
4.1%
R 1317
 
3.7%
Other values (34) 11734
32.7%
Common
ValueCountFrequency (%)
0 19021
27.5%
1 12123
17.6%
2 8013
11.6%
9 4612
 
6.7%
3 4486
 
6.5%
7 4367
 
6.3%
5 4270
 
6.2%
6 4188
 
6.1%
4 3977
 
5.8%
8 3953
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 104928
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19021
18.1%
1 12123
 
11.6%
2 8013
 
7.6%
9 4612
 
4.4%
3 4486
 
4.3%
7 4367
 
4.2%
5 4270
 
4.1%
6 4188
 
4.0%
U 3992
 
3.8%
4 3977
 
3.8%
Other values (45) 35879
34.2%

error
Categorical

CONSTANT
MISSING

Distinct1
Distinct (%)33.3%
Missing8737
Missing (%)> 99.9%
Memory size341.6 KiB
{'status': 404, 'message': 'analysis not found'}

Length

Max length48
Median length48
Mean length48
Min length48

Characters and Unicode

Total characters144
Distinct characters22
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row{'status': 404, 'message': 'analysis not found'}
2nd row{'status': 404, 'message': 'analysis not found'}
3rd row{'status': 404, 'message': 'analysis not found'}

Common Values

ValueCountFrequency (%)
{'status': 404, 'message': 'analysis not found'} 3
 
< 0.1%
(Missing) 8737
> 99.9%

Length

2022-12-17T21:11:57.686081image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-17T21:11:58.132130image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
status 3
16.7%
404 3
16.7%
message 3
16.7%
analysis 3
16.7%
not 3
16.7%
found 3
16.7%

Most occurring characters

ValueCountFrequency (%)
s 18
12.5%
' 18
12.5%
15
 
10.4%
a 12
 
8.3%
t 9
 
6.2%
n 9
 
6.2%
u 6
 
4.2%
: 6
 
4.2%
4 6
 
4.2%
e 6
 
4.2%
Other values (12) 39
27.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 87
60.4%
Other Punctuation 27
 
18.8%
Space Separator 15
 
10.4%
Decimal Number 9
 
6.2%
Open Punctuation 3
 
2.1%
Close Punctuation 3
 
2.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 18
20.7%
a 12
13.8%
t 9
10.3%
n 9
10.3%
u 6
 
6.9%
e 6
 
6.9%
o 6
 
6.9%
y 3
 
3.4%
d 3
 
3.4%
f 3
 
3.4%
Other values (4) 12
13.8%
Other Punctuation
ValueCountFrequency (%)
' 18
66.7%
: 6
 
22.2%
, 3
 
11.1%
Decimal Number
ValueCountFrequency (%)
4 6
66.7%
0 3
33.3%
Space Separator
ValueCountFrequency (%)
15
100.0%
Open Punctuation
ValueCountFrequency (%)
{ 3
100.0%
Close Punctuation
ValueCountFrequency (%)
} 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 87
60.4%
Common 57
39.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 18
20.7%
a 12
13.8%
t 9
10.3%
n 9
10.3%
u 6
 
6.9%
e 6
 
6.9%
o 6
 
6.9%
y 3
 
3.4%
d 3
 
3.4%
f 3
 
3.4%
Other values (4) 12
13.8%
Common
ValueCountFrequency (%)
' 18
31.6%
15
26.3%
: 6
 
10.5%
4 6
 
10.5%
{ 3
 
5.3%
, 3
 
5.3%
0 3
 
5.3%
} 3
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 144
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 18
12.5%
' 18
12.5%
15
 
10.4%
a 12
 
8.3%
t 9
 
6.2%
n 9
 
6.2%
u 6
 
4.2%
: 6
 
4.2%
4 6
 
4.2%
e 6
 
4.2%
Other values (12) 39
27.1%

Interactions

2022-12-17T21:11:30.395813image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:23.333797image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:30.678000image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:40.063939image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:45.934307image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:51.949510image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:58.831177image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:06.418777image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:12.831217image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:17.415102image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:21.689962image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:26.082495image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:30.724798image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:23.714495image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:31.544965image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:40.521310image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:46.393017image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:52.807137image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:59.357528image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:07.377303image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:13.208982image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:17.878765image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:21.989523image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:26.392172image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:31.073574image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:24.058764image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:32.553260image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:40.955738image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:46.911713image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:53.274497image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:59.957275image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:08.352938image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:13.612310image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:18.235085image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:22.479885image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:26.775861image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:31.388842image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:24.543585image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:33.437305image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:41.427002image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:47.478351image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:53.685115image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:00.404264image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:08.901000image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:13.995979image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:18.539242image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:22.786381image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:27.138045image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:31.712531image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:25.038486image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:34.080432image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:41.925978image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:48.006235image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:54.104143image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:01.032216image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:09.561636image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:14.317284image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:18.844464image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:23.122356image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:27.465699image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:32.090108image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:25.572764image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:34.901685image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:42.494037image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:48.448749image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:54.787290image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:01.688326image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:10.021339image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:14.713058image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:19.283875image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:23.553056image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:27.855266image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:32.464172image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:26.241139image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:35.725852image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:42.908948image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:48.953584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:55.324696image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:02.407856image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:10.393825image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:15.129878image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:19.671296image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:23.907979image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:28.194656image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:32.984693image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:27.283886image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:36.580883image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:43.362670image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:49.554158image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:55.839207image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:03.167262image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:10.825309image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:15.472839image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:20.032455image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:24.329493image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:28.539753image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:33.393676image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:28.292832image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:37.409976image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:43.889397image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:50.119406image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:56.550086image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:03.785877image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:11.186402image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:15.850082image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:20.419257image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:24.709964image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:28.982266image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:33.715019image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:29.090041image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:37.950326image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:44.376395image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:50.596081image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:57.111499image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:04.351598image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:11.781318image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:16.280033image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:20.709420image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:25.065485image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:29.354370image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:34.017460image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:29.390954image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:38.702732image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:44.932717image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:51.011998image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:57.568876image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:05.009135image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:12.120463image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:16.650847image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:21.011210image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:25.362740image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:29.718189image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:34.345410image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:29.883676image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:39.612312image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:45.441442image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:51.483384image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:10:58.272111image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:05.820533image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:12.461097image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:17.074567image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:21.340222image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:25.711455image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-17T21:11:30.070482image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-12-17T21:11:58.498409image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-12-17T21:11:59.108111image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-17T21:11:59.722949image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-17T21:12:00.344411image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-17T21:12:00.852833image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-12-17T21:12:01.691403image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-17T21:11:35.002383image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-17T21:11:36.793561image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-12-17T21:11:37.814188image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

artist_nametrack_nameplayshashdanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempotypeiduritrack_hrefanalysis_urlduration_mstime_signatureid_hashalbumrelease_dateexplicitpopularityisrcerror
0!!!So We Can FuckTrue40826686141ecbeb2a9a032ba9b17ae9966d3b9285f651017bb33d4ed988f7170.8090.4669.0-9.4910.00.0744580.0000720.6146450.1097510.7200120.002track4knd2gQyr2DTRLfJDHcyMSspotify:track:4knd2gQyr2DTRLfJDHcyMShttps://api.spotify.com/v1/tracks/4knd2gQyr2DTRLfJDHcyMShttps://api.spotify.com/v1/audio-analysis/4knd2gQyr2DTRLfJDHcyMS2965004.040826686141ecbeb2a9a032ba9b17ae9966d3b9285f651017bb33d4ed988f717I'm Sick of This/So We Can Fuck2020-05-01False28GBBPW2000087NaN
100110100 010101000000 871 0003True8fe126c7d28ac465eacb644ec878ea554744d6dd7d2fef696df6e847bd1336940.3950.2602.0-16.1530.00.0468840.5584720.6141040.1088540.0724152.546track7ns3vcnzAxjCZVYwlwazahspotify:track:7ns3vcnzAxjCZVYwlwazahhttps://api.spotify.com/v1/tracks/7ns3vcnzAxjCZVYwlwazahhttps://api.spotify.com/v1/audio-analysis/7ns3vcnzAxjCZVYwlwazah754173.08fe126c7d28ac465eacb644ec878ea554744d6dd7d2fef696df6e847bd1336948712020-12-25False33GBXNG2015003NaN
203 GreedoSubstance (We Woke Up)True5139a49bdfa4749b67c074870911e75976d58b32b076d1d7a72f4813edfe76a30.7300.5403.0-5.9790.00.0506930.3096880.0000000.1484200.3270141.954track2S8gTIectkC846PHdsAshCspotify:track:2S8gTIectkC846PHdsAshChttps://api.spotify.com/v1/tracks/2S8gTIectkC846PHdsAshChttps://api.spotify.com/v1/audio-analysis/2S8gTIectkC846PHdsAshC2366194.05139a49bdfa4749b67c074870911e75976d58b32b076d1d7a72f4813edfe76a3Substance (We Woke Up)2021-02-03True62USLD91730821NaN
3070 ShakeGuilty Conscience - Tame Impala RemixTrue2b34ac0f1ca8fac70845b6cb894bac839ab229454203ef29b3d2bee9058fd5600.4150.8781.0-3.6500.00.2776320.0548670.0034740.2004890.3090191.777track5i5fCpsnqDJ9AfeObgd0gWspotify:track:5i5fCpsnqDJ9AfeObgd0gWhttps://api.spotify.com/v1/tracks/5i5fCpsnqDJ9AfeObgd0gWhttps://api.spotify.com/v1/audio-analysis/5i5fCpsnqDJ9AfeObgd0gW2149864.02b34ac0f1ca8fac70845b6cb894bac839ab229454203ef29b3d2bee9058fd560Guilty Conscience (Tame Impala Remix)2020-07-24False60USUM72013244NaN
4070 ShakeGuilty Conscience - Tame Impala Remix ExtendedTrue283330c524139861dda440b4ea23424f6930f9b900372737d91e20213fe1c6a40.4260.8509.0-4.7541.00.2358620.0703650.0062700.1371500.2130191.928track7qDUOLnOLYKTwzvCJDnYRfspotify:track:7qDUOLnOLYKTwzvCJDnYRfhttps://api.spotify.com/v1/tracks/7qDUOLnOLYKTwzvCJDnYRfhttps://api.spotify.com/v1/audio-analysis/7qDUOLnOLYKTwzvCJDnYRf2870664.0283330c524139861dda440b4ea23424f6930f9b900372737d91e20213fe1c6a4Guilty Conscience (Tame Impala Remix)2020-07-24False38USUM72015141NaN
5070 ShakeGuilty Conscience - Tame Impala Remix InstrumentalTrueab6360e389e3f5d1195762455462751a2cafccfcb6ad56ddbf025a8c999b0b730.5180.8159.0-7.0161.00.0898410.0729720.6386910.1612680.0716191.862track2nwc1w2yyOXPCyTaphRQGNspotify:track:2nwc1w2yyOXPCyTaphRQGNhttps://api.spotify.com/v1/tracks/2nwc1w2yyOXPCyTaphRQGNhttps://api.spotify.com/v1/audio-analysis/2nwc1w2yyOXPCyTaphRQGN2859334.0ab6360e389e3f5d1195762455462751a2cafccfcb6ad56ddbf025a8c999b0b73Guilty Conscience (Tame Impala Remix)2020-07-24False33USUM72015142NaN
6070 ShakeThe PinesTrue3c8a3f0b83fbbf1283e05bc8e8359c3b2e0167a2846aeae1ab32a3b823b9b7dd0.5220.4399.0-7.0370.00.0339180.0339180.0000090.3534700.1760163.927track0uTw7TNnYn64XmCAo5jr0cspotify:track:0uTw7TNnYn64XmCAo5jr0chttps://api.spotify.com/v1/tracks/0uTw7TNnYn64XmCAo5jr0chttps://api.spotify.com/v1/audio-analysis/0uTw7TNnYn64XmCAo5jr0c2136134.03c8a3f0b83fbbf1283e05bc8e8359c3b2e0167a2846aeae1ab32a3b823b9b7ddModus Vivendi2020-01-17False46USUM71925530NaN
7100 gecs800db cloudTrue5a4fe1451e61705a1b59338ee79fb383eb3efd7f57d41dc32eb2f56f1422a4460.4340.8468.0-5.3101.00.3987760.1292720.0000160.1266330.4880141.617track5N7X3lGWDi4P0v2h9Vs9mFspotify:track:5N7X3lGWDi4P0v2h9Vs9mFhttps://api.spotify.com/v1/tracks/5N7X3lGWDi4P0v2h9Vs9mFhttps://api.spotify.com/v1/audio-analysis/5N7X3lGWDi4P0v2h9Vs9mF1039404.05a4fe1451e61705a1b59338ee79fb383eb3efd7f57d41dc32eb2f56f1422a4461000 gecs and The Tree of Clues2020-07-10False34USAT22000181NaN
8100 gecsbloodstainsTrued02f429f5cf8b05621bad75db5f28706f81333c12848cb95bf6dbab3b3b270ba0.5470.39810.0-9.1421.00.4324320.1672080.0004360.2684990.406087.373track1j581GCDFQ9P8xd6fyjgLtspotify:track:1j581GCDFQ9P8xd6fyjgLthttps://api.spotify.com/v1/tracks/1j581GCDFQ9P8xd6fyjgLthttps://api.spotify.com/v1/audio-analysis/1j581GCDFQ9P8xd6fyjgLt1290154.0d02f429f5cf8b05621bad75db5f28706f81333c12848cb95bf6dbab3b3b270ba100 gecs2017-01-02False50TCADL1841679NaN
9100 gecshand crushed by a malletTruecbe55ee5f173ea3090ec1a5bac0b7829b32295e7f869103c13068034843e2c850.6590.4731.0-7.3060.00.1177830.2421620.0000000.2468600.788084.505track7CUkeiG7QtB7tPU9f8SANSspotify:track:7CUkeiG7QtB7tPU9f8SANShttps://api.spotify.com/v1/tracks/7CUkeiG7QtB7tPU9f8SANShttps://api.spotify.com/v1/audio-analysis/7CUkeiG7QtB7tPU9f8SANS1266164.0cbe55ee5f173ea3090ec1a5bac0b7829b32295e7f869103c13068034843e2c851000 gecs2019-05-31True61USAT22000422NaN
artist_nametrack_nameplayshashdanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempotypeiduritrack_hrefanalysis_urlduration_mstime_signatureid_hashalbumrelease_dateexplicitpopularityisrcerror
8730KetatoTetri Pikri | White thoughtFalse4d028bf041160194f7af913fe8ecd9311848790e3c4385c094efbe25c2c9b23d0.6460.2400.0-14.2560.00.0373920.6703900.0017880.4612150.194120.088track5IczFH0YW5FBh3ktC38LFBspotify:track:5IczFH0YW5FBh3ktC38LFBhttps://api.spotify.com/v1/tracks/5IczFH0YW5FBh3ktC38LFBhttps://api.spotify.com/v1/audio-analysis/5IczFH0YW5FBh3ktC38LFB2772934.04d028bf041160194f7af913fe8ecd9311848790e3c4385c094efbe25c2c9b23dTetri Pikri | White thought2021-11-19False0QZPLR2151261NaN
8731rabb1txdВремяFalsea6006c08e6df404e832e6d47a83a542a16a43fd5b5bfb6497286849064fe89a80.7050.3926.0-12.8981.00.0348840.1371500.0335310.1061600.572135.051track4tb2CF9l1fjcJMJmxLDoZospotify:track:4tb2CF9l1fjcJMJmxLDoZohttps://api.spotify.com/v1/tracks/4tb2CF9l1fjcJMJmxLDoZohttps://api.spotify.com/v1/audio-analysis/4tb2CF9l1fjcJMJmxLDoZo1150144.0a6006c08e6df404e832e6d47a83a542a16a43fd5b5bfb6497286849064fe89a8Die!2022-09-09True0FRX762205151NaN
8732ZHFTardFalse382ba5558596db5daffa90aebd4dcc1704d0d7aeef69e2fa4935f239739e73220.8200.36810.0-13.9400.00.4401890.3804890.0000690.0925790.258130.173track7BgrbTWJSfin28VS2Q3Oocspotify:track:7BgrbTWJSfin28VS2Q3Oochttps://api.spotify.com/v1/tracks/7BgrbTWJSfin28VS2Q3Oochttps://api.spotify.com/v1/audio-analysis/7BgrbTWJSfin28VS2Q3Ooc1920004.0382ba5558596db5daffa90aebd4dcc1704d0d7aeef69e2fa4935f239739e7322Nocturne2021-08-17True0QZK6G2189774NaN
8733IndochineBelfastFalsefd541de2613f6b3eed2e4623dcf777991fe69f568e8542abbb94ca572155a2050.5290.7891.0-6.1231.00.0291700.0090290.0003600.0946740.484133.972track63H7WN1Ws41f3JimFOAK3Aspotify:track:63H7WN1Ws41f3JimFOAK3Ahttps://api.spotify.com/v1/tracks/63H7WN1Ws41f3JimFOAK3Ahttps://api.spotify.com/v1/audio-analysis/63H7WN1Ws41f3JimFOAK3A3650404.0fd541de2613f6b3eed2e4623dcf777991fe69f568e8542abbb94ca572155a205Black City Parade2014-06-23False23FRZ081200624NaN
8734Maja SowińskaMarzenieFalse02e2e120620e13e82326ff48a17b3b2b0c9de47cad6f0a3f425ec9dfc87e2df80.3480.6217.0-7.0811.00.0861780.0333380.0000000.0998450.184140.015track5lHISKy6SUsvCrJ9N213vsspotify:track:5lHISKy6SUsvCrJ9N213vshttps://api.spotify.com/v1/tracks/5lHISKy6SUsvCrJ9N213vshttps://api.spotify.com/v1/audio-analysis/5lHISKy6SUsvCrJ9N213vs3311604.002e2e120620e13e82326ff48a17b3b2b0c9de47cad6f0a3f425ec9dfc87e2df8Marzenie2021-12-02False7PL88K2100011NaN
8735Spa Relaxation & SpaAmbience for Wellness CentersFalse754c476a2cf988f206f1c111f0fe513e612b7ec503a4bd995851c182684422070.3090.1757.0-22.3151.00.0522130.6871290.6280750.1034590.191199.005track2zAR76nTQC9FfOBYBwH1Oqspotify:track:2zAR76nTQC9FfOBYBwH1Oqhttps://api.spotify.com/v1/tracks/2zAR76nTQC9FfOBYBwH1Oqhttps://api.spotify.com/v1/audio-analysis/2zAR76nTQC9FfOBYBwH1Oq1631994.0754c476a2cf988f206f1c111f0fe513e612b7ec503a4bd995851c18268442207Solo Piano for Wellness Resorts2020-01-23False0QZFQ51086855NaN
8736OkresCósmikaFalse24c7adf823b82a2ebbc92e4b4ca42e89881eb9a5bd06b3302d29a3390e5172640.7340.8001.0-4.7581.00.3074850.2021240.0000000.0787190.85689.839track0Jb48CoUcPKbR4Vgnq3Is1spotify:track:0Jb48CoUcPKbR4Vgnq3Is1https://api.spotify.com/v1/tracks/0Jb48CoUcPKbR4Vgnq3Is1https://api.spotify.com/v1/audio-analysis/0Jb48CoUcPKbR4Vgnq3Is12350004.024c7adf823b82a2ebbc92e4b4ca42e89881eb9a5bd06b3302d29a3390e517264Cósmika2021-05-07False0usdy42182638NaN
8737Cid MoreiraEsforço em Confiança em DeusFalsefcd775b5b77532e4dc70341bd2e7ad5363f4ca3a7221b6e0aa9d1adb6d5206850.7090.1990.0-12.6580.00.3818550.6544060.0000000.0847090.579113.683track1OoERbByXE3D1uyx4yYbxHspotify:track:1OoERbByXE3D1uyx4yYbxHhttps://api.spotify.com/v1/tracks/1OoERbByXE3D1uyx4yYbxHhttps://api.spotify.com/v1/audio-analysis/1OoERbByXE3D1uyx4yYbxH1333863.0fcd775b5b77532e4dc70341bd2e7ad5363f4ca3a7221b6e0aa9d1adb6d520685Recados da Bíblia, Vol. 52018-08-03False1BRGLD1800083NaN
8738Années soixante dix orchestraLady LayFalse84cf8f58e2299d8de2704867efbc3e4f8ef114cca56b4e811ed7865afae147e70.6620.8059.0-8.5050.00.0314990.0073830.0000000.0922140.874127.287track47uXK5g9QWIzAKzbU9mbDdspotify:track:47uXK5g9QWIzAKzbU9mbDdhttps://api.spotify.com/v1/tracks/47uXK5g9QWIzAKzbU9mbDdhttps://api.spotify.com/v1/audio-analysis/47uXK5g9QWIzAKzbU9mbDd1908044.084cf8f58e2299d8de2704867efbc3e4f8ef114cca56b4e811ed7865afae147e7Les Succès des Années 70, Vol. 32011-04-01False14FRR261003510NaN
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Duplicate rows

Most frequently occurring

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